What is the purpose of this server?
It demonstrates how knowledge can be extracted from a Neo4J database via the Model Context Protocol. The included documents are dummy guidelines.
@simon-k
基本情報
MCP Server that serves knowledge from a Neo4J Database
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": {
"knowledge-mcp": {
"command": "docker",
"args": [
"run",
"\\"
]
}
}
}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。
knowledge-mcp is a prototype MCP server that serves knowledge from a Neo4J database, using dummy guidelines as example documents. It is intended for developers working with MCP-compatible clients like Cursor or Claude Desktop.
Run Neo4J in a Docker container, run the included data loader to populate documents, build the MCP server binary, then configure it in an MCP client (e.g., Cursor) by providing the server path, OpenAI API key, and Neo4J credentials. The server communicates over stdio transport.
It demonstrates how knowledge can be extracted from a Neo4J database via the Model Context Protocol. The included documents are dummy guidelines.
No, it is a prototype and is not ready for production use.
You need a running Neo4J instance (e.g., via Docker), an OpenAI API key, and you must build the .NET 9 server binary.
Add an MCP server in Cursor Settings with type `stdio`, command set to the built server binary path, and args containing your OpenAI key, Neo4J URI (`neo4j://localhost:7687`), username (`neo4j`), and password.
Yes, it works in any client that supports MCP, such as Claude Desktop.

Search saved employee handbooks from your AI assistant. Four read-only tools list authorized handbooks, browse policies, search content, and read source pages with citation links. Uses scoped bearer tokens and Streamable
Local MCP server and CLI that keeps a Git-versioned, model-written index of a codebase and its database schema, so coding agents start each task with the whole system in view.
Groundwork: company memory for your AI tools. Docs public. Source closed. Public proof needs no account.

Orcha is a unified context layer for AI tools and agents. It stores and indexes and exposes organizational, team, or individual knowledge. Files, structured databases, and connected sources live in one workspace, and age

Sooveryn gives Claude Code a team of AI personas that remembers your project. Soov, the partner and team lead, keeps your decisions and how you work in a lasting memory, and finds them again by words and by meaning, from
コメント