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WisdomForge MCP Server

@hadv

基本情報

カテゴリ

データベース

ライセンス

MIT

トランスポート

stdio

作者

hadv

WisdomForge MCP Server について

A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.

WisdomForge MCP Server の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

WisdomForge 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 WisdomForge?

WisdomForge is a knowledge management system that stores and retrieves experiences, insights, best practices, and lessons learned using a vector database. It integrates with the Model Context Protocol (MCP) for use in AI IDEs like Cursor and Claude Desktop, and works with Qdrant or Chroma for efficient embedding-based retrieval.

How to use WisdomForge?

  1. Clone the repository, install dependencies, and build the project with npm install && npm run build.
  2. Create a .env file from the template and configure required environment variables: DATABASE_TYPE (qdrant/chroma), COLLECTION_NAME, and the corresponding database URL/key. Optionally set HTTP_SERVER=true for HTTP mode.
  3. Integrate with Cursor AI by adding the provided MCP configuration to your ~/.cursor/mcp.json file, or configure Claude Desktop following the run-mcp.sh script instructions.
  4. Use the MCP tools store_knowledge and retrieve_knowledge_context to store and retrieve domain knowledge.

Key features of WisdomForge

  • Intelligent knowledge management and retrieval via vector search
  • Support for multiple knowledge types (best practices, lessons learned, insights, experiences)
  • Configurable database selection between Qdrant and Chroma
  • Uses Qdrant’s built-in FastEmbed for efficient embedding generation
  • Domain knowledge storage and retrieval for AI assistants
  • Deployable to Smithery.ai cloud platform

Use cases of WisdomForge

  • Storing and retrieving team best practices and lessons learned
  • Providing contextual domain knowledge to AI coding assistants
  • Managing institutional knowledge for onboarding and training
  • Powering knowledge retrieval in MCP‑compatible tools like Cursor and Claude

よくある質問

1

What runtime and dependencies does WisdomForge require?

It requires Node.js 20.x or later, npm 10.x or later, and either a Qdrant or Chroma vector database instance.

2

How do I configure which database to use?

Set the `DATABASE_TYPE` environment variable to `qdrant` or `chroma`, and supply the corresponding URL (and API key for Qdrant). The `COLLECTION_NAME` variable defines the vector collection.

3

Where is the knowledge data stored?

All knowledge is stored in the configured external vector database (Qdrant or Chroma). WisdomForge does not persist data locally.

4

What transport modes are supported?

WisdomForge supports MCP transport via shell scripts (`run-wisdomforge-mcp.sh` for Cursor, `run-mcp.sh` for Claude) and an optional HTTP server mode enabled by `HTTP_SERVER=true`.

5

How do I authenticate to a remote Qdrant instance?

If you are using Qdrant, set the `QDRANT_API_KEY` environment variable with your API key. No authentication is documented for Chroma in the README.

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