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Chatcrystal MCP Server

@ZengLiangYi

リポジトリ

基本情報

カテゴリ

その他

トランスポート

stdio

作者

ZengLiangYi

投稿者

Rayner Zeng

Chatcrystal MCP Server について

概要はまだありません

Chatcrystal MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "chatcrystal": {
      "command": "crystal",
      "args": [
        "mcp"
      ]
    }
  }
}

Chatcrystal MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is ChatCrystal?

ChatCrystal collects conversations from AI coding tools (Claude Code, Cursor, Codex CLI, Trae, GitHub Copilot), distills them into structured notes using LLMs, and builds a local searchable knowledge base from your real problem-solving history. It is for developers who want to turn AI-assisted coding conversations into reusable knowledge.

How to use ChatCrystal?

Install the Windows desktop app from GitHub Releases, or run npm install -g chatcrystal and use crystal serve -d plus crystal import to start a CLI/Web server at http://localhost:3721. For a Docker cloud deployment, clone the repo and run docker compose up -d. Configure an LLM provider and an embedding provider in Settings, then import conversations from supported tools.

Key features of ChatCrystal

  • Imports AI coding conversations from local tool data directories.
  • Distills conversations into structured notes with titles, summaries, and tags.
  • Searches knowledge semantically with embeddings and relation-aware results.
  • Builds a knowledge graph across related notes and decisions.
  • Exposes both CLI and MCP tools for agent recall and write-back.
  • Runs locally with configurable LLM and embedding providers.

Use cases of ChatCrystal

  • Turning Claude Code or Cursor chat logs into a searchable personal knowledge base.
  • Reusing past solutions by semantically searching for similar problems.
  • Building a knowledge graph to visualize relationships between coding decisions.
  • Using MCP tools to let AI agents recall and contribute to experience notes.
  • Synchronizing conversation history from multiple devices to a self-hosted cloud instance.

よくある質問

1

What data does ChatCrystal store and where?

It stores normalized conversations and structured notes locally. In the Docker cloud deployment, data lives in the `chatcrystal-data` volume at `/data` inside the container. The CLI scans local histories, parses them locally, and uploads normalized conversations to the cloud—the cloud never scans the local filesystem.

2

What are the runtime requirements?

Node.js >=20 is required. You also need an LLM provider for summarization and an embedding provider for semantic search. Large language models and embedding models are configured separately.

3

How does authentication work?

In Docker cloud, on first start without `CHATCRYSTAL_API_TOKEN`, you enter a setup code from container logs via the Web UI and choose a shared API token. The token can be rotated using `crystal token rotate`. If `CHATCRYSTAL_API_TOKEN` is set, it overrides stored auth.

4

Can ChatCrystal be used with MCP agents?

Yes. Run `crystal mcp` to start the MCP stdio server. See [docs/MCP.md](docs/MCP.md) for more details.

5

Does ChatCrystal support multiple AI coding tools?

Yes. Supported sources include Claude Code, Cursor, Codex CLI, Trae, and GitHub Copilot. You import from a device using `crystal import` or the desktop app.

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