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/dspy-mcp-tool-integration

Use for MCP tools with DSPy, Model Context Protocol servers, dspy.Tool.from_mcp_tool, and ReAct agents over MCP-compatible tools.

From plugin
dspy-skills
12323 skills
Install
$ npx -y skills add OmidZamani/dspy-skills --skill dspy-mcp-tool-integration --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/dspy-mcp-tool-integration

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use for MCP tools with DSPy, Model Context Protocol servers, dspy.Tool.from_mcp_tool, and ReAct agents over MCP-compatible tools.

SKILL.md

dspy-mcp-tool-integration.SKILL.md
name: dspy-mcp-tool-integration
version: "1.0.0"
dspy-compatibility: "3.2.1"
tags: ["agent", "production"]
requires-extras: ["dspy[mcp]"]
description: Use for MCP tools with DSPy, Model Context Protocol servers, dspy.Tool.from_mcp_tool, and ReAct agents over MCP-compatible tools.
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep

DSPy MCP Tool Integration

Goal

Connect an MCP server with the MCP Python client, convert its tools to `dspy.Tool`, and use them in an async DSPy agent.

Install

pip install -U "dspy[mcp]>=3.2.1,<3.3"

DSPy converts tools but does not manage MCP connections. Keep the `ClientSession` alive for as long as the DSPy tools are in use.

Streamable HTTP Server

import asyncio
import dspy
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

async def main():
    async with streamablehttp_client("http://localhost:8000/mcp") as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            response = await session.list_tools()
            tools = [dspy.Tool.from_mcp_tool(session, tool) for tool in response.tools]

            agent = dspy.ReAct("task -> result", tools=tools, max_iters=5)
            output = await agent.acall(task="Check the weather in Tokyo")
            print(output.result)

asyncio.run(main())

Local Stdio Server

import asyncio
import dspy
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def main():
    params = StdioServerParameters(
        command="python3",
        args=["path/to/server.py"],
        env=None,
    )

    async with stdio_client(params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            response = await session.list_tools()
            tools = [dspy.Tool.from_mcp_tool(session, tool) for tool in response.tools]
            agent = dspy.ReAct("question -> answer", tools=tools, max_iters=5)
            print((await agent.acall(question="What is 25 + 17?")).answer)

asyncio.run(main())

Best Practices

1. Use `acall()` because MCP tools are asynchronous. 2. Initialize the session before listing tools. 3. Keep tool descriptions precise at the MCP server boundary. 4. Apply authentication and authorization before exposing sensitive tools. 5. Set a modest `max_iters` and trace tool use in production.

Related Skills

  • Build agents: [dspy-react-agent-builder](../dspy-react-agent-builder/SKILL.md)
  • Configure native function calling: [dspy-adapters-multimodal](../dspy-adapters-multimodal/SKILL.md)
  • Add async runtime patterns: [dspy-production-deployment](../dspy-production-deployment/SKILL.md)

Official Documentation

  • **DSPy MCP guide**: https://dspy.ai/learn/programming/mcp/
  • **DSPy MCP tutorial**: https://dspy.ai/tutorials/mcp/
  • **MCP Python SDK**: https://github.com/modelcontextprotocol/python-sdk
Read more
Ships withdspy-skills

A Claude Code plugin containing 22 focused skills for programming, optimizing, evaluating, and deploying LLM applications with DSPy. Stable DSPy baseline: 3.2.1, released May 5, 2026.

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Python
Language
MIT
License
2mo ago
Last commit
9mo ago
Created

Repo: OmidZamani/dspy-skills

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