Codeseeker MCP server

CodeSeeker

Four-layer hybrid search and knowledge graph for AI coding assistants. BM25 + vector embeddings + RAPTOR directory summaries + graph expansion — fused into a single MCP tool that gives Claude, Copilot, and Cursor a real understanding of your codebase.

Works with Claude Code, GitHub Copilot (VS Code 1.99+), Cursor, Windsurf, and Claude Desktop. One command to index; the Claude Code plugin keeps it in sync from there.

Built by PragmaWorks as part of Generative Specification — the discipline for building software with AI that doesn't drift. Two sibling MCP servers compose with this one, each solving a different half of the same problem:

They are independent: install one, or all three.

When you stop needing this

CodeSeeker solves a retrieval problem, and that problem is shrinking.

If you run a spec-driven loop with a real harness — a specification precise enough that a stateless model derives from it, and quality gates that verify the result against a live system — or an intent-driven one, your assistant is being handed the structure it would otherwise have to go find. A sentinel tool that routes actions instead of sprawling across a dozen endpoints removes another slice of the searching. Frontier models with better navigation and larger effective context remove more. Past a certain point, semantic search over your own code stops being the bottleneck, and this server becomes something you could uninstall without noticing.

That is the intended outcome, not a defect: a method that works should make its own scaffolding unnecessary. Most teams do not start there, though, and until the process is in place CodeSeeker is the cheaper substitute — one command instead of a discipline.

If you would rather have the process than the substitute, start with the field guide: Generative Specification — Field Guide (PDF).

The Problem

AI assistants are powerful editors, but they navigate code like a tourist:

• Grep finds text — not meaning. "find authentication logic" returns every file containing the word "auth"
• File reads are isolated — Claude sees a file but not its dependencies, callers, or the patterns your team established
• No memory of your project — every session starts from scratch

CodeSeeker fixes this. It indexes your codebase once and gives AI assistants a queryable knowledge graph they can use on every turn.

How It Works

A 4-stage pipeline runs on every query:

Shortened. The full README is on GitHub.