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MindsDB MCP 交互技能 · leoyeai | DecimalAI

Databy leoyeai

MindsDB MCP 交互技能

MindsDB MCP服务器交互技能，用于通过自然语言查询和操作200+企业级数据源。当用户需要查询数据库、分析数据、创建AI模型、连接数据源（MySQL、PostgreSQL、MongoDB、Excel、CSV、Gmail、Slack等）、执行SQL查询、进行数据预测、构建知识库（RAG）、智能问答、文档检索或任何与数据库交互的任务时使用此技能。即使没有明确提到MindsDB，只要涉及数据库操作、数据分析、数据查询、知识库构建、AI问答或需要连接多个数据源的场景，都应该使用此技能。

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%77%of test cases passed +59 pts

Without skill4 of 22

With skill17 of 22

13 fixed4 passed either way5 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v120 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI need to attach an external MySQL database located at 'db.example.com:3306' into our unified data query environment. Please provide the connection setup syntax and verification step.

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add leoyeai-mindsdb-mcp-skill
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install leoyeai-mindsdb-mcp-skill

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/leoyeai-mindsdb-mcp-skill/SKILL.md https://app.decimal.ai/s/leoyeai-mindsdb-mcp-skill/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add leoyeai-mindsdb-mcp-skill

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-mindsdb-mcp-skill")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add leoyeai-mindsdb-mcp-skill

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md https://app.decimal.ai/s/leoyeai-mindsdb-mcp-skill/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-mindsdb-mcp-skill/SKILL.md

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/leoyeai-mindsdb-mcp-skill/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-mindsdb-mcp-skill.json

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/8/2026
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

MindsDB MCP 交互技能

概述

MindsDB是一个AI大规模数据查询引擎，支持连接200+企业级数据源，通过MCP（Model Context Protocol）协议提供统一的数据库操作接口。本技能帮助你通过自然语言与各种数据库交互。

架构说明 / Architecture Overview

使用方式 / Usage Pattern

本技能采用 Agent + MCP 架构，与直接使用MindsDB有所不同：

This skill uses an Agent + MCP architecture, which differs from direct MindsDB usage:

直接使用MindsDB / Direct MindsDB Usage:

用户 → MindsDB GUI/SQL → 数据源/AI模型
User → MindsDB GUI/SQL → Data Sources/AI Models

本技能方式 / This Skill's Approach:

用户（自然语言）→ Claude Agent → MindsDB MCP Server → 数据源/AI模型
User (Natural Language) → Claude Agent → MindsDB MCP Server → Data Sources/AI Models

核心优势 / Key Advantages

自然语言交互 / Natural Language Interaction
- 无需编写SQL语句 / No SQL knowledge required
- 用日常语言描述需求 / Describe needs in everyday language
- Agent自动生成并执行SQL / Agent automatically generates and executes SQL
智能推理 / Intelligent Reasoning
- 自动理解用户意图 / Automatically understands user intent
- 智能选择最优方案 / Intelligently selects optimal approach
- 提供解释和建议 / Provides explanations and recommendations
多步骤自动化 / Multi-step Automation
- 自动完成复杂工作流 / Automatically completes complex workflows
- 无需手动执行多个步骤 / No need to manually execute multiple steps
- 端到端任务处理 / End-to-end task processing

示例对比 / Example Comparison

传统方式 / Traditional Approach:

-- 需要手动编写SQL / Need to write SQL manually
CREATE DATABASE my_postgres 
WITH ENGINE = 'postgres',
PARAMETERS = {"host": "127.0.0.1", ...};

SELECT * FROM my_postgres.products;

本技能方式 / This Skill's Approach:

用户: "连接到Postgres数据库并查询产品信息"
User: "Connect to Postgres database and query product information"

Agent自动完成:
1. 连接数据库 / Connect to database
2. 生成SQL / Generate SQL
3. 执行查询 / Execute query
4. 返回结果 / Return results

技术架构 / Technical Architecture

Claude Agent (对话界面 / Chat Interface)
    ↓ (自然语言 / Natural Language)
MCP Client (内置 / Built-in)
    ↓ (MCP协议 / MCP Protocol)
MindsDB MCP Server (MindsDB提供的MCP接口 / MindsDB MCP Interface)
    ↓ (SQL/API)
MindsDB Server (核心引擎 / Core Engine)
    ↓ (连接器 / Connectors)
数据源 / Data Sources (MySQL, Postgres, 文件 / Files, etc.)

适用场景 / Use Cases

✅ 适合 / Suitable for: 非技术人员、快速原型开发、自动化工作流 Non-technical users, rapid prototyping, automated workflows
✅ 适合 / Suitable for: 需要自然语言交互的场景 Scenarios requiring natural language interaction
⚠️ 可选 / Optional: 需要精细控制SQL的高级用户 Advanced users requiring fine-grained SQL control

核心能力

1. 数据源连接

MindsDB支持连接多种数据源：

关系型数据库: MySQL, PostgreSQL, SQL Server, Oracle, SQLite
NoSQL数据库: MongoDB, Redis, Cassandra
云存储: AWS S3, Google Cloud Storage, Azure Blob
文件格式: CSV, Excel, JSON, Parquet
SaaS应用: Gmail, Slack, Salesforce, Shopify
其他: ClickHouse, Snowflake, BigQuery, Elasticsearch

2. 自然语言查询

将自然语言转换为SQL查询，支持：

数据检索和筛选
聚合分析（SUM, AVG, COUNT等）
多表关联查询
复杂条件查询
数据排序和分页

3. AI模型创建

使用MindsDB创建预测模型：

时间序列预测
分类任务
回归分析
异常检测
推荐系统

4. 知识库构建（RAG）

基于检索增强生成技术构建智能知识库：

向量存储和检索
文档导入和管理
智能问答Agent
多模态支持（文本、图像、语音）
嵌入模型配置

5. 智能分析

MindsDB的AI驱动的智能分析能力：

自动数据探索: 自动发现数据模式、异常和趋势
智能洞察生成: 基于数据自动生成业务洞察和建议
预测性分析: 使用机器学习模型进行预测和趋势分析
异常检测: 自动识别数据中的异常模式和异常值
因果分析: 分析变量之间的因果关系
推荐系统: 基于用户行为和模式生成个性化推荐
文本分析: 自然语言处理、情感分析、主题建模
时序分析: 时间序列预测、趋势分解、周期性分析
聚类分析: 无监督学习，发现数据中的自然分组
关联规则挖掘: 发现数据项之间的关联关系

6. 数据库管理

创建和管理数据库
表结构操作
数据导入导出
数据转换和清洗

工作流程

第一步：理解用户需求

当用户提出数据库相关需求时，首先理解：

想要查询什么数据
数据源类型
查询条件或分析目标
期望的输出格式

第二步：构建查询

根据用户需求，选择合适的操作：

简单查询: 使用自然语言描述直接查询
复杂查询: 构建SQL语句
AI预测: 创建MindsDB模型
数据源操作: 连接或管理数据源

第三步：执行查询

使用MindsDB MCP工具执行操作，处理结果。

第四步：结果展示

以清晰易懂的方式展示结果，包括：

数据表格
统计摘要
可视化建议
后续操作建议

常用操作模式

模式1: 自然语言查询

用户输入: "查询上个月销售额最高的产品" 处理:

理解查询意图
转换为MindsDB查询
执行并返回结果

模式2: SQL查询

用户输入: "SELECT * FROM orders WHERE date > '2024-01-01'" 处理:

验证SQL语法
执行查询
返回结果集

模式3: 创建预测模型

用户输入: "预测下个月的销售额" 处理:

确定目标变量
选择合适的模型引擎
训练模型
生成预测

模式4: 连接数据源

用户输入: "连接到MySQL数据库" 处理:

获取连接参数
创建数据源连接
验证连接
返回连接状态

模式5: 构建知识库

用户输入: "创建一个技术文档知识库" 处理:

创建RAG数据库
导入文档内容
配置嵌入模型
创建智能Agent

模式6: 智能问答

用户输入: "根据技术文档回答设备报错0xE1怎么处理" 处理:

使用Agent查询知识库
检索相关文档
生成回答
返回结果

模式7: 智能数据分析

用户输入: "分析销售数据，找出增长趋势和异常" 处理:

自动探索数据
识别模式和趋势
检测异常值
生成洞察报告
提供可视化建议

模式8: 预测性分析

用户输入: "预测下季度的销售额" 处理:

选择合适的预测模型
训练模型
生成预测结果
提供置信区间
解释预测依据

模式9: 推荐系统

用户输入: "为用户推荐可能感兴趣的产品" 处理:

分析用户行为数据
创建推荐模型
生成个性化推荐
计算推荐得分
返回推荐列表

最佳实践

1. 查询优化

使用索引列进行筛选
限制返回的数据量
避免全表扫描
合理使用聚合函数

2. 错误处理

捕获并解释错误信息
提供修复建议
记录错误日志
优雅降级

3. 安全考虑

验证SQL注入风险
限制敏感数据访问
使用参数化查询
审计查询日志

4. 性能优化

批量操作优于单条操作
使用连接池
缓存常用查询
异步执行长时间任务

输出格式

查询结果

使用清晰的表格格式展示数据，包括：

列名
数据类型
行数
执行时间

错误信息

提供详细的错误说明：

错误类型
错误位置
可能原因
解决方案

操作状态

显示操作进度和状态：

进度百分比
当前步骤
预计剩余时间
完成状态

示例场景

场景1: 销售数据分析

用户: "分析2024年各地区的销售趋势" 操作:

查询2024年销售数据
按地区分组
计算趋势指标
生成分析报告

场景2: 客户行为预测

用户: "预测哪些客户可能会流失" 操作:

创建流失预测模型
训练模型
生成预测结果
提供干预建议

场景3: 库存管理

用户: "查询库存不足的产品" 操作:

查询当前库存
对比安全库存水平
识别缺货产品
生成补货建议

场景4: 技术支持知识库

用户: "创建一个技术文档知识库，用于智能问答" 操作:

创建RAG数据库
导入PDF技术文档
配置嵌入模型
创建技术支持Agent
测试问答功能

场景5: 智能客服系统

用户: "根据产品手册回答用户问题" 操作:

查询知识库
检索相关文档
使用Agent生成回答
提供解决方案

场景6: 智能销售分析

用户: "分析销售数据，找出增长机会和风险" 操作:

自动探索销售数据
识别增长趋势和模式
检测异常和风险信号
生成业务洞察
提供行动建议

场景7: 客户流失预测

用户: "预测哪些客户可能会流失，并提供挽留建议" 操作:

创建流失预测模型
分析客户行为模式
识别高风险客户
生成挽留策略
预测挽留效果

场景8: 产品推荐

用户: "为用户推荐他们可能喜欢的产品" 操作:

分析用户历史行为
创建推荐模型
生成个性化推荐
计算推荐置信度
返回推荐列表和理由

场景9: 工业设备监控

用户: "获取工业设备的运行情况" 操作:

连接TDengine时序数据库
查询实时监测数据（温度、压力、流量等）
查询设备运行状态（电机、阀门、传感器等）
分析设备效率和运行参数
检测异常指标和告警
生成运行报告和建议

说明: 示例中的表名和字段名仅为演示，Agent会自动适配你的实际数据库结构。

示例查询:

用户: "查询1号车间过去24小时的温度和压力数据"
Agent:
1. 连接TDengine数据库
2. 自动查询表结构，发现实际表名和字段
3. 根据实际结构生成SQL:
   SELECT ts, temperature, pressure 
   FROM your_actual_table_name 
   WHERE location_id = 'workshop_001' 
     AND ts > NOW() - INTERVAL 24 HOUR
4. 返回结果和趋势分析

用户: "检查所有设备的运行状态，找出异常设备"
Agent:
1. 查询设备状态表（自动发现实际表名）
2. 根据实际字段名筛选异常设备
3. 分析异常原因
4. 提供维护建议

场景10: 工业物联网监控

用户: "分析工厂生产线的能耗情况" 操作:

连接时序数据库（TDengine/InfluxDB）
查询设备能耗数据
计算能耗趋势和峰值
识别能耗异常
生成节能建议

注意事项

数据源连接: 需要用户提供正确的连接参数
权限管理: 确保有足够的数据库权限
数据安全: 不要暴露敏感信息
性能考虑: 大数据量查询可能需要较长时间
模型训练: AI模型训练需要时间和计算资源

实际案例

工业设备监控案例

案例背景: 使用MindsDB AI Agent分析TDengine时序数据库中的工业设备数据

数据规模:

约200个不同设备
约10,000条小时级数据记录
7天的监测数据

设备类型:

温度监测设备 (TEMP系列)
压力监测设备 (PRESS系列)
控制阀门设备 (VALVE系列)
电机设备 (MOTOR系列)
流量监测设备 (FLOW系列)

Agent执行的操作:

自动发现表结构 (sql_db_list_tables, sql_db_schema)
查询设备列表 (约200个设备)
统计设备数据 (数据点数、平均值、标准差)
分析设备相关性
生成综合分析报告

关键发现:

Agent自动适配TDengine特有语法
自动处理错误并调整查询策略
智能生成分析报告
支持自然语言交互

详细案例: 参见 industrial-monitoring-case.md

扩展功能

高级查询

窗口函数
递归查询
存储过程调用
自定义函数

集成能力

与其他MCP服务器集成
API调用
数据管道
实时流处理

可视化

图表生成
仪表板创建
报告导出
数据导出

故障排除

常见问题

连接失败: 检查连接参数和网络
查询超时: 优化查询或增加超时时间
权限错误: 检查数据库权限设置
语法错误: 验证SQL语法
性能问题: 分析查询计划，添加索引

调试技巧

启用查询日志
使用EXPLAIN分析查询
分步执行复杂查询
测试子查询

参考资料

官方文档

MindsDB官方文档: https://docs.mindsdb.com
MCP协议规范: https://modelcontextprotocol.io

技能参考文档

详细的技术文档位于 references/ 目录：

mindsdb-tools.md: MindsDB MCP工具完整参考
- 可用工具列表和参数说明
- 支持的数据源
- 查询示例
- 模型创建示例
- 错误处理指南
data-sources.md: 200+数据源配置指南
- 关系型数据库配置（MySQL、PostgreSQL、Oracle等）
- NoSQL数据库配置（MongoDB、Redis、Elasticsearch等）
- 云数据库配置（AWS RDS、Google Cloud SQL、Azure等）
- 文件格式导入（CSV、Excel、JSON、Parquet）
- SaaS应用连接（Gmail、Slack、Salesforce等）
- 连接池、SSL/TLS、代理配置
sql-examples.md: SQL查询示例大全
- 基础查询（SELECT、WHERE、排序、限制）
- 聚合查询（COUNT、SUM、AVG、GROUP BY）
- 连接查询（INNER JOIN、LEFT JOIN、多表连接）
- 子查询（标量、IN、EXISTS、FROM子查询）
- 窗口函数（ROW_NUMBER、RANK、LAG/LEAD）
- CASE表达式和实用函数
- 性能优化技巧
sdk-api.md: SDK和API参考
- Python SDK使用（连接、创建模型、查询、异步操作）
- JavaScript SDK使用（连接、创建模型、查询）
- REST API调用（认证、创建模型、预测、执行SQL）
- MySQL协议连接
- MongoDB协议连接
- 高级功能（版本管理、监控、批量操作）
- 错误处理和最佳实践
knowledge-base.md: 知识库（RAG）构建指南
- RAG核心概念和组件
- 创建RAG数据库和配置嵌入模型
- 从PDF、文本、数据库、网页导入知识
- 创建智能Agent（基础、高级、多知识库）
- 查询知识库和智能问答
- 向量存储优化（Chroma、FAISS）
- 多模态支持（图像、语音）
- 实际应用场景（技术支持、产品推荐、法律、医疗）
- 性能优化、安全、监控
mlops-advanced.md: MLOps和高级功能指南
- 模型部署（基础、批量、实时推理端点）
- 模型监控（性能、数据漂移、预测质量、资源使用）
- 模型版本管理（创建、比较、回滚、标签）
- A/B测试（创建、监控、选择获胜模型）
- MLflow集成（连接、记录模型、加载模型、注册）
- dbt集成（配置、宏、数据质量检查）
- Airflow集成（DAG创建、监控任务）
- 批处理（批量预测、训练、评估）
- 模型解释（特征重要性、SHAP值、预测解释）
- 自动化工作流（自动重训练、模型选择、告警）
- 性能优化（缓存、批处理、查询优化）
- 安全和合规（访问控制、数据隐私、审计）
streaming-pipelines.md: 实时流处理和数据管道指南
- 实时流处理（Kafka、AWS Kinesis、RabbitMQ）
- 流式数据消费和实时预测
- 实时聚合分析和统计
- 数据管道（ETL/ELT、增量管道、事件驱动）
- 时序数据处理（InfluxDB、时序预测、异常检测）
- 图数据库集成（Neo4j、图查询、图神经网络）
- 多租户支持（租户隔离、资源配额、租户监控）
- 集群管理（节点配置、负载均衡、集群监控）
- 高可用性（主从复制、故障转移、数据备份）
- 性能优化（流处理优化、索引优化、查询优化）
- 监控和告警（流处理监控、管道监控、告警配置）
intelligent-analysis.md: 智能分析指南
- 自动数据探索（数据概览、质量检查、分布分析、相关性分析）
- 智能洞察生成（自动洞察发现、趋势分析、模式识别、业务洞察报告）
- 预测性分析（时间序列预测、回归预测、分类预测、预测结果查询）
- 异常检测（统计异常检测、时序异常检测、异常分析）
- 因果分析（因果推断、因果关系发现、反事实分析）
- 推荐系统（协同过滤、内容推荐、混合推荐、推荐查询）
- 文本分析（情感分析、主题建模、命名实体识别、文本分类）
- 时序分析（趋势分解、周期性分析、变点检测）
- 聚类分析（K-Means、层次聚类、DBSCAN、聚类分析）
- 关联规则挖掘（Apriori、FP-Growth、关联规则查询）
- 智能分析工作流（端到端分析、自动化报告）
- 可视化集成（图表生成、仪表板创建）
- 性能优化（分析缓存、并行分析、增量分析）

其他参考

SQL参考手册
数据源连接指南
机器学习最佳实践
MLOps工作流程
实时数据处理指南
智能分析最佳实践

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MindsDB MCP 交互技能

概述

架构说明 / Architecture Overview

使用方式 / Usage Pattern

核心优势 / Key Advantages

示例对比 / Example Comparison

技术架构 / Technical Architecture

适用场景 / Use Cases

核心能力

1. 数据源连接

2. 自然语言查询

3. AI模型创建

4. 知识库构建（RAG）

5. 智能分析

6. 数据库管理

工作流程

第一步：理解用户需求

第二步：构建查询

第三步：执行查询

第四步：结果展示

常用操作模式

模式1: 自然语言查询

模式2: SQL查询

模式3: 创建预测模型

模式4: 连接数据源

模式5: 构建知识库

模式6: 智能问答

模式7: 智能数据分析

模式8: 预测性分析

模式9: 推荐系统

最佳实践

1. 查询优化

2. 错误处理

3. 安全考虑

4. 性能优化

输出格式

查询结果

错误信息

操作状态

示例场景

场景1: 销售数据分析

场景2: 客户行为预测

场景3: 库存管理

场景4: 技术支持知识库

场景5: 智能客服系统

场景6: 智能销售分析

场景7: 客户流失预测

场景8: 产品推荐

场景9: 工业设备监控

场景10: 工业物联网监控

注意事项

实际案例

工业设备监控案例

扩展功能

高级查询

集成能力

可视化

故障排除

常见问题

调试技巧

参考资料

官方文档

技能参考文档

其他参考

SkillSafety

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