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Agent Protocol · borghei | DecimalAI

agent toolingby borghei

Agent Protocol

borghei/agent-protocol

Source borghei/claude-skills ↗License NOASSERTION

Design AI agent communication protocols: MCP tool schemas, A2A, function calling, and inter- agent messaging.

SkillScore73Maintained

Automatic

AI Engineer pack

Last verified 8/20/2026

What it changes

Passed 16 of 22 test cases with the skill, and 11 of 22 without it.

Without skill50%

With skill73%

Checks it passed that the model alone missed

Lists assumptions at top of artifact.
Specifies Agent Cards for capability advertising.
Specifies HTTP with JSON-RPC for transport layer.

and 4 more in the full report

5 fixed11 passed either way6 still failing0 broke

Pass-rate lift: +23 pp
Tokens: +77%
Agent turns: 0%
Response time: +5%

Model gemini-3.6-flashMeasured 8/20/2026v116 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/borghei-agent-protocol/SKILL.md https://app.decimal.ai/s/borghei-agent-protocol/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add borghei-agent-protocol, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/borghei-agent-protocol/ and pins it in .decimal/skills.lock

decimalai skills add borghei-agent-protocol

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/borghei-agent-protocol/SKILL.md https://app.decimal.ai/s/borghei-agent-protocol/SKILL.md

Raw file: /s/borghei-agent-protocol/SKILL.md ↗ · machine-readable summary at /s/borghei-agent-protocol.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 5

Prompt

Just draft it. We are building custom tools for a Claude desktop client running locally via stdio with no authentication required. Create a JSON Schema input definition for a weather lookup tool with location as required and units as optional string enum.

What was checked, without and with the skill
Checked	Without skill	With skill
Lists assumptions at top of artifact.	Missed	Passed
Uses JSON Schema format for tool input argument validation.	Passed	Passed
Formats tool definition for Model Context Protocol (MCP).	Missed	Passed

case-04 · gemini-3.6-flash · 8/20/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md84 lines8 filesincludes scripts

Agent Protocol

The agent designs tool schemas for MCP, Google A2A, and OpenAI Function Calling protocols. It implements transport selection, capability discovery, authentication flows (OAuth 2.1, API keys), structured error handling, rate limiting, and protocol bridges for heterogeneous agent ecosystems.

Core Capabilities

Protocol selection & comparison — MCP (tool/resource/prompt serving), Google A2A (agent cards, task lifecycle, streaming), OpenAI Function Calling (JSON Schema, parallel calls, strict mode), LangChain/LangGraph tools, and custom WebSocket/gRPC messaging
Tool schema design — JSON Schema input/output validation, semantic naming conventions, description engineering for LLM comprehension, required vs optional parameters, enum and default strategies
Transport & discovery — stdio/SSE/WebSocket for MCP, HTTP+JSON-RPC for A2A, agent card capability advertisement, health checking, and protocol version negotiation
Security & authentication — OAuth 2.1 flows, API key rotation and scoping, request signing, per-identity rate limiting, and audit logging for inter-agent calls

When to Use

Designing tool interfaces for LLM-powered agents
Building MCP servers that expose APIs to Claude, Cursor, or other clients
Implementing agent-to-agent communication in multi-agent systems
Bridging between different agent protocols (MCP to A2A, etc.)
Standardizing tool calling patterns across a team or organization
Debugging agent tool selection failures

Clarify First

Before designing the protocol, confirm these inputs. If any is unknown or vague, ASK — do not assume:

Protocol target — MCP, A2A, OpenAI Function Calling, or LangChain Tools (drives the entire schema and transport design via the Decision Framework below)
Transport & client — which client consumes it (stdio/SSE/WebSocket for MCP, HTTP+JSON-RPC for A2A)
Auth & trust boundary — in-process vs cross-organization (determines OAuth 2.1 vs API key vs none, plus rate limiting and signing)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Decision Framework

What are you building?
│
├─ Tools for a single LLM client (Claude, Cursor, Copilot)
│  └─ Use MCP — it's the native protocol for tool serving
│
├─ Agent-to-agent communication across organizations
│  └─ Use A2A — designed for cross-boundary agent discovery and delegation

Track record

Benchmark

16 / 22

test cases passed, vs 11 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

borghei / claude-skills

engineering/agent-protocol/SKILL.md

View on GitHub ↗

License MIT + Commons ClauseImported 8/3/2026Version v1Bundled 7 files · scripts

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Agent Protocol

Core Capabilities

When to Use

Clarify First

Decision Framework

Track record

Benchmark

Adoption

SkillSafety

Source

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