dspy-mcp-tool-integration
This skill should be used when the user asks to "use MCP tools with DSPy", "connect an MCP server to ReAct", "convert MCP tools to DSPy tools", mentions Model Context Protocol, `dspy.Tool.from_mcp_tool`, streamable HTTP MCP transport, stdio MCP servers, or needs to expose MCP-compatible tools to a DSPy agent.
git clone --depth 1 https://github.com/OmidZamani/dspy-skills /tmp/dspy-mcp-tool-integration && cp -r /tmp/dspy-mcp-tool-integration/skills/dspy-mcp-tool-integration ~/.claude/skills/dspy-mcp-tool-integrationSKILL.md
# 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
```bash
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
```python
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
```python
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-sdkUse this skill when you need to QA audit and fix a plugin skill file. Provides a methodology for verifying skill content against official documentation, fixing issues in-place, and producing verification reports.
This skill should be used when the user asks to "choose a DSPy adapter", "use JSONAdapter", "use XMLAdapter", "enable native function calling", "send images, audio, or files to DSPy", mentions `dspy.ChatAdapter`, `dspy.JSONAdapter`, `dspy.XMLAdapter`, `dspy.Image`, `dspy.Audio`, `dspy.File`, structured outputs, or multimodal DSPy signatures.
This skill should be used when the user asks to "compose DSPy modules", "use Ensemble optimizer", "combine multiple programs", "use dspy.MultiChainComparison", mentions "ensemble voting", "module composition", "sequential pipelines", or needs to build complex multi-module DSPy programs with ensemble patterns or multi-chain comparison.
This skill should be used when the user asks to "use BetterTogether", "combine prompt optimization and fine-tuning", "sequence DSPy optimizers", "run prompt then weight optimization", mentions `dspy.BetterTogether`, strategy strings such as "p -> w -> p", or needs to compose multiple DSPy teleprompters into an evaluated optimization sequence.
This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.
This skill should be used when the user asks to "create custom DSPy module", "design a DSPy module", "extend dspy.Module", "build reusable DSPy component", mentions "custom module patterns", "module serialization", "stateful modules", "module testing", or needs to design production-quality custom DSPy modules with proper architecture, state management, and testing.
This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.
This skill should be used when the user asks to "build local DSPy retrieval", "use dspy.Embedder", "use dspy.Embeddings", "save an embeddings index", "add FAISS retrieval", mentions semantic search, hosted embeddings, local embedding models, `EmbeddingsWithScores`, or needs a DSPy retriever over an application-owned text corpus.