jghiringhelli/codeseeker
Developer toolsこのページは AI により自動翻訳されています。技術的な詳細は原文をご確認ください。 原文を見る
MCP server with semantic search + knowledge graph for Claude Code, Cursor, and Copilot
概要
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 , (VS Code 1.99+), , , and . One command to index; the Claude Code plugin keeps it in sync from there. Built by PragmaWorks as part of — 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. 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.
README
以下は原文 README の抜粋です。原文を見る。
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:
| what it gives your assistant | |
|---|---|
| CodeSeeker (this) | where things are — semantic search and a knowledge graph over your code |
Chronicle npm i -g chronicle-mcp |
what happened before — tiered memory that survives context resets |
Forgecraft npm i -g forgecraft-mcp |
how it should be built — SOLID, testing, architecture and CI/CD standards |
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:
インストール
npx -y codeseeker serve --mcp設定
{
"mcpServers": {
"codeseeker": {
"command": "npx",
"args": ["-y", "codeseeker", "serve", "--mcp"],
"env": { "CODESEEKER_STORAGE_MODE": "embedded" }
}
}
}