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MCP LangChain Bridge

Expose any LangChain chain, agent, or tool as an MCP server. The point is to take orchestration logic that already exists in LangChain — RAG chains, agentic workflows, custom tools — and make it agent-callable from Claude Desktop, Cursor, or any MCP client.

What you get

• Auto-schema: each LangChain tool's args_schema becomes the MCP tool's input schema. Pydantic v2 generation built in.

• Retry policies: configurable retry/backoff per tool, with circuit-breaker behavior on persistent failures.

• Timeouts: per-tool execution timeout, configurable via decorator or env var.

• Tracing: OpenTelemetry spans on every tool call. Drops into LangSmith if LANGSMITH_API_KEY is set.

• Output validation: results validated against Pydantic schemas before returning to the agent.

Related MCP server: LangChain Anthropic MCP Server

Usage

from mcp_langchain_bridge import bridge
from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun

server = bridge.create_server(
    name="research-tools",
    tools=[
        DuckDuckGoSearchRun(),
        WikipediaQueryRun(),
        # Any LangChain BaseTool subclass works
    ],
    retry={"max_attempts": 3, "backoff": "exponential"},
    timeout=30.0,
)

server.run()

Why this matters

LangChain has the largest tool ecosystem in the agent space. MCP has the cleanest agent-host integration. Bridging the two avoids rewriting in either direction:

• You don't need to port your retrieval chain to native MCP tool definitions

• You don't need to abandon Claude Desktop because your existing stack is LangChain

• LangGraph state machines remain unchanged — the MCP layer wraps the entrypoint

Limitations

• Streaming responses from chains are buffered into the MCP response (MCP spec doesn't yet support streaming for tool calls). Long-running chains should be checkpointed externally.

• LangChain custom callback handlers fire as expected, but UI updates targeted at notebook environments won't surface to the MCP client.

License

MIT.