MCP Server Client

@muralianand12345

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

muralianand12345

MCP Server Client について

概要はまだありません

#mcp-server-client#api #streamlit

MCP Server Client 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。

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MCP Server Client 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 Mcp_server_client?

A system that provides a chat interface (Streamlit) backed by a NestJS API, integrating multiple MCP (Model Context Protocol) servers for tool execution, RAG (Retrieval-Augmented Generation) with a vector store, and OpenAI for embeddings and chat completions.

How to use Mcp_server_client?

—

Key features of Mcp_server_client

Streamlit frontend for user interaction
NestJS API with chat, agent, tool agent, and RAG services
Integration with multiple MCP servers via SSE
Retrieval-augmented generation using PGVector
OpenAI API for embeddings and chat completions
Storage support: AWS S3, PGVector, PostgreSQL

Use cases of Mcp_server_client

Building a chat application that leverages multiple MCP servers as tools
Implementing retrieval-augmented generation with a vector database
Using an agent-based architecture to orchestrate MCP tool calls

よくある質問

What technologies are used in the system?

The system uses Streamlit for the frontend, NestJS for the API, MCP servers as tools, OpenAI for AI services, and storage backends including AWS S3 (via LocalStack), PGVector, and PostgreSQL.

How do MCP servers communicate with the API?

The Tool Agent Service in the NestJS API communicates with MCP servers via SSE (Server-Sent Events) over `/sse` endpoints.

What storage backends are supported?

The architecture supports AWS S3 (via LocalStack), PGVector (a PostgreSQL vector extension), and standard PostgreSQL.

Does the system use OpenAI?

Yes, the OpenAIService interacts with the OpenAI API for chat completions and embeddings. The RAG service uses embeddings, and the Agent service uses chat completions.

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