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Knolia Connect Server MCP Server

@Danejw

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

Danejw

Knolia Connect Server MCP Server について

MCP server to connect users

Knolia Connect Server 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": {
    "connect-server": {
      "command": "python",
      "args": [
        "connect_mcp_server/main.py"
      ]
    }
  }
}

Knolia Connect Server 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 knolia-connect-server?

knolia-connect-server is an MCP server that connects users with compatible companions for romantic and emotional relationships using AI-powered vector matching. It is built with FastMCP, Supabase with pgvector, and OpenAI embeddings, and is part of the Knolia platform.

How to use knolia-connect-server?

Install dependencies from requirements.txt, then run python connect_mcp_server/main.py or via Uvicorn with uvicorn connect_mcp_server.main:mcp --reload. Docker build and run with an .env file is also supported. Exposed FastMCP tools: create_profile, update_profile, delete_profile, find_matches, and explain_match.

Key features of knolia-connect-server?

  • AI-powered vector matching with 1536‑dimensional embeddings
  • Structured connect profiles for goals, personality, and preferences
  • Cosine similarity ranking for match scoring
  • Explainable matches with similarity score and shared tags
  • Full CRUD operations for user profiles
  • FastMCP integration for tool-based server interaction

Use cases of knolia-connect-server?

  • Matching users seeking deep emotional connections based on values and personality
  • Facilitating introductions between compatible companions
  • Providing transparency through explainable match suggestions
  • Helping combat loneliness via intentional relationship discovery

よくある質問

1

What dependencies are required?

Python packages in `requirements.txt`, a Supabase database with `pgvector`, and an OpenAI API key for embedding generation.

2

How are matches computed?

Cosine similarity between each user’s 1536‑dimensional embedding and the embeddings of all other opted‑in users.

3

What data is stored?

User profile data (relationship goals, personality tags, sexual preferences, location, embedded vector, opt‑in status) in Supabase.

4

Can I run knolia-connect-server locally?

Yes. Install dependencies, set environment variables, and run via Uvicorn or Docker as described in the README.

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