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Model Context Protocol (MCP) Server

@shaswata-das

Repository

About Model Context Protocol (MCP) Server

No overview available yet

#mcp-server#ai-services#json-rpc

Basic information

Category

Other

License

MIT license

Runtime

python

Transports

stdio

Publisher

shaswata-das

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "basic-mcp-server-shaswata-das": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Model Context Protocol (MCP) Server?

A modular Model Context Protocol server for AI services that supports multiple AI providers (Claude, OpenAI, and a mock service) and transport options (stdio, TCP, and WebSocket). It provides a JSON‑RPC 2.0 interface for tool calling and resource management, with dynamic service selection on a per‑request basis.

How to use Model Context Protocol (MCP) Server?

Clone the repository, create a virtual environment, install dependencies with pip install -e ., and configure API keys via the .env file. Run the server with python mcp_server.py (defaults to stdio mode) or use --tcp or --websocket flags to choose other transports. Interact using the provided example clients (examples/example_client.py and examples/websocket_client.html).

Key features of Model Context Protocol (MCP) Server

  • Multiple AI services (Claude, OpenAI, mock) selectable per request
  • Three transport options: stdio, TCP, and WebSocket
  • JSON‑RPC 2.0 compliant interface for predictable interactions
  • Dynamic service selection without restarting the server
  • Streaming response support for compatible transports
  • Modular architecture for easy extension with new services and transports
  • Qdrant vector database integration for embeddings and semantic search

Use cases of Model Context Protocol (MCP) Server

  • Build AI‑powered command‑line tools that can switch between models at runtime
  • Integrate AI assistants into web browsers using WebSocket transport
  • Test and develop AI workflows locally with the mock service, then swap in real APIs
  • Deploy a single server that serves multiple AI backends behind a load balancer

FAQ from Model Context Protocol (MCP) Server

What AI services are supported?

The server supports Claude

Comments

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