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Build Model Context Protocol (MCP) servers that expose tools, resources, and prompts to AI agents.
Passed 18 of 22 test cases with the skill, and 16 of 22 without it.
and 1 more in the full report
2 fixed16 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
In a Python MCP server entry point function, how are the streams obtained from `stdio_server()` passed to `server.run()` along with initialization options?
| Checked | Without skill | With skill |
|---|---|---|
| Passes unpacked streams into server.run along with create_initialization_options. | Missed | Passed |
The instructions your model reads when the skill loads.
Create Model Context Protocol (MCP) servers to expose tools and resources to AI agents.
search_database, send_email)from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp import types
server = Server("my-server")
@server.list_tools()
async def list_tools() -> list[types.Tool]:
return [
types.Tool(
name="my_tool",
description="What this tool does and when to use it",
inputSchema={
"type": "object",
"properties": {
"param": {"type": "string", "description": "Parameter description"}
},
"required": ["param"]
}
)
]
@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[types.TextContent]:
if name == "my_tool":
result = do_something(arguments["param"])
return [types.TextContent(type="text", text=str(result))]
test cases passed, vs 16 / 22 without
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