Semcode MCP Server

@GoodbyePlanet

Basic information

About Semcode MCP Server

An MCP (Model Context Protocol) server providing hybrid semantic search over code across a set of GitHub repositories that you list in config.yaml. It parses symbols with Tree-sitter and indexes both code and git commit history, so AI clients can query them by natural language or

#mcp #embeddings#codesearch#semantic-search #rag

Semcode MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "semcode": {
      "transport": "http",
      "url": "http://localhost:8090/mcp"
    }
  }
}

Semcode MCP Server tools

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Overview

What is Semcode?

Semcode is an MCP server that provides hybrid semantic search over code across a set of GitHub repositories you list in config.yaml. It parses code symbols using Tree-sitter, indexes both source code and git commit history, and enables AI clients to query them by natural language or symbol name.

How to use Semcode?

Install Python 3.12+, Docker, and a GitHub token. Clone the repo, run uv sync, copy .env.example to .env and set GITHUB_TOKEN, then copy config.example.yaml to config.yaml and list the repositories to index. Start the server with make docker-up-jina (local embeddings) or make docker-up (hosted provider). Connect AI clients by pointing them at http://localhost:8090/mcp.

Key features of Semcode

Hybrid retrieval combining dense embeddings and BM25
Incremental indexing—only changed files are re-embedded
Supports 19 programming languages with framework-aware parsing
Optional git commit history indexing with full diffs
MCP tools for search, symbol lookup, and reindexing
HTTP API for triggering index from CI/CD pipelines

Use cases of Semcode

Querying codebases by natural language (e.g., "where do we publish order events?")
Finding symbol definitions and usages across multiple repositories
Retrieving full source context for symbols within a file
Searching git commit history to understand code evolution
Generating architectural overviews of services via MCP prompts

Frequently asked questions

What embedding providers does Semcode support?

The default is Jina Code V2 via a local TEI container (no API key needed). You can also use Voyage, OpenAI, or Ollama by setting the `EMBEDDINGS_PROVIDER` environment variable.

How does incremental indexing work?

Files are skipped when their Git blob SHA matches the last indexed version. Files that no longer exist or parse to zero symbols are cleaned up automatically. Pass `force: true` to re-embed everything.

What are the runtime requirements?

Python 3.12+, Docker (for Qdrant and optionally Jina TEI), and a GitHub personal access token with `Contents: read` on the target repositories.

Where does indexed data live?

Vectors and metadata are stored in a local Qdrant instance running in a Docker container with persistent volumes. Embeddings cache is also persisted.

How do I connect an AI client to Semcode?

Point your MCP client (Claude Desktop, Claude Code CLI, Cursor, etc.) to `http://localhost:8090/mcp` using the HTTP transport. The README includes configuration examples for several clients.

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