Learn how to build a Model Context Protocol (MCP) server that enables AI models to understand and manipulate code using Codegen’s powerful tools.

This guide will walk you through creating an MCP server that can provide semantic code search

View the full code in our examples repository

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Setup:

Install the MCP python library

uv pip install mcp

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Step 1: Setting Up Your MCP Server

First, let’s create a basic MCP server using Codegen’s MCP tools:

server.py

from codegen import Codebase
from mcp.server.fastmcp import FastMCP
from typing import Annotated
# Initialize the codebase
codebase = Codebase.from_repo(".")

# create the MCP server using FastMCP
mcp = FastMCP(name="demo-mcp", instructions="Use this server for semantic search of codebases")


if __name__ == "__main__":
    # Initialize and run the server
    print("Starting demo mpc server...")
    mcp.run(transport="stdio")

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Step 2: Create the search tool

Let’s implement the semantic search tool.

server.py

from codegen.extensions.tools.semantic_search import semantic_search

....

@mcp.tool('codebase_semantic_search', "search codebase with the provided query")
def search(query: Annotated[str, "search query to run against codebase"]):
  codebase = Codebase("provide location to codebase", language="provide codebase Language")
  # use the semantic search tool from codegen.extensions.tools OR write your own
  results = semantic_search(codebase=codebase, query=query)
  return results

....

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Run Your MCP Server

You can run and inspect your MCP server with:

mcp dev server.py

If you’d like to integrate this into an IDE checkout out this setup guide

And that’s a wrap, chime in at our community Slack if you have questions or ideas for additional MCP tools/capabilities

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