Dspy MCP

Connect DSPy Agents to MCP Tool Servers

Guide the user through connecting DSPy agents to MCP (Model Context Protocol) servers, discovering tools at runtime, and wiring them into ReAct or CodeAct agents.

What is MCP integration in DSPy

DSPy can consume tools from any MCP-compatible server using dspy.Tool.from_mcp_tool(). This lets your agents use tools hosted externally -- databases, file systems, web APIs, or custom services -- without writing Python wrappers for each one. The MCP server handles execution; DSPy handles reasoning.

When to use MCP

Step 1: Install dependencies

pip install dspy mcp

Step 2: Connect to an MCP server

MCP servers communicate via stdio. Use StdioServerParameters to configure the server command and stdio_client to establish the connection:

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

lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

# Configure the MCP server to connect to
server_params = StdioServerParameters(
    command="npx",
    args=["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/dir"],
)

Step 3: Discover and convert tools

Inside an async context, connect to the server, list available tools, and convert them to DSPy tools:

import asyncio

async def build_agent():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the MCP connection
            await session.initialize()

            # Discover available tools
            mcp_tools = await session.list_tools()
            print(f"Found {len(mcp_tools.tools)} tools")

            # Convert MCP tools to DSPy tools
            dspy_tools = [
                dspy.Tool.from_mcp_tool(session, tool)
                for tool in mcp_tools.tools
            ]

            # Build a ReAct agent with the discovered tools
            agent = dspy.ReAct(
                "question -> answer",
                tools=dspy_tools,
            )

            # Use acall() for async tool execution
            result = await agent.acall(question="List all Python files in the project")
            print(result.answer)

asyncio.run(build_agent())

Step 4: Wire into ReAct or CodeAct

The converted tools work exactly like native DSPy tools:

async def run_agent_with_mcp():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            mcp_tools = await session.list_tools()
            dspy_tools = [
                dspy.Tool.from_mcp_tool(session, tool)
                for tool in mcp_tools.tools
            ]

            # ReAct agent
            agent = dspy.ReAct(
                "task -> result",
                tools=dspy_tools,
                max_iters=10,
            )

            # Must use acall() because MCP tools are async
            result = await agent.acall(task="Find the largest file and summarize it")
            return result

Important: Use await agent.acall() (not agent()) because MCP tool calls are async operations.

Step 5: Combining MCP tools with local tools

Mix MCP-discovered tools with locally-defined Python tools:

def calculate(expression: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expression))  # use a safe evaluator in production

async def build_hybrid_agent():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            mcp_tools = await session.list_tools()
            dspy_tools = [
                dspy.Tool.from_mcp_tool(session, tool)
                for tool in mcp_tools.tools
            ]

            # Combine MCP tools with local tools
            all_tools = dspy_tools + [calculate]

            agent = dspy.ReAct("question -> answer", tools=all_tools)
            result = await agent.acall(question="How many lines in main.py divided by 3?")
            return result

Step 6: Error handling

MCP connections can fail. Wrap the connection in proper error handling:

from mcp import McpError

async def safe_agent_call(question: str):
    try:
        async with stdio_client(server_params) as (read, write):
            async with ClientSession(read, write) as session:
                await session.initialize()

                mcp_tools = await session.list_tools()
                dspy_tools = [
                    dspy.Tool.from_mcp_tool(session, tool)
                    for tool in mcp_tools.tools
                ]

                agent = dspy.ReAct("question -> answer", tools=dspy_tools)
                return await agent.acall(question=question)

    except McpError as e:
        print(f"MCP server error: {e}")
        return None
    except ConnectionError:
        print("Could not connect to MCP server")
        return None

Gotchas

1. Claude forgets acall() and uses synchronous agent(). MCP tools are async -- you must use await agent.acall() or you get runtime errors. DSPy cannot call async MCP tools from a sync context.
2. The entire agent must run inside the async with block. The MCP session is only valid inside the context manager. If you build the tools inside and call the agent outside, the session is closed and tool calls fail.
3. Claude hardcodes tool lists instead of discovering them. The point of MCP is runtime discovery. Always use session.list_tools() to get the current tool set -- MCP servers can add/remove tools dynamically.
4. MCP server must be running before you connect. StdioServerParameters launches the server process. If the command fails (e.g., npx not installed, package not found), you get a cryptic connection error. Test the server command manually first.
5. Claude nests too many async context managers. Keep the pattern flat -- one stdio_client context and one ClientSession context. Do not add extra wrappers.

Additional resources

• DSPy MCP tutorial
• MCP specification
• For API details, see reference.md
• For worked examples, see examples.md

Optimizing tool descriptions

Tool descriptions matter for agent performance. The GEPA paper (arxiv 2507.19457) includes an MCP adapter that can automatically optimize tool descriptions -- not just task instructions -- through the same reflective evolution process. If your agent struggles to pick the right tool or misuses tool parameters, consider optimizing tool descriptions with /dspy-gepa.

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Defining tools from Python functions -- see /dspy-tools
• ReAct agents that use tools -- see /dspy-react
• CodeAct agents for code execution -- see /dspy-codeact
• Action-taking AI from a problem-first perspective -- see /ai-taking-actions
• Async execution patterns -- see /dspy-async
• Install /ai-do if you do not have it -- it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do