mcp_toolsuite

alberttrann/mcp_toolsuite

3.3

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The Model Context Protocol (MCP) server is a versatile platform designed to facilitate communication between AI agents and tools, enabling dynamic tool discovery and usage.

The Model Context Protocol (MCP) server is a robust platform that serves as the backbone for AI agent communication and tool integration. It allows clients to connect and interact with a suite of AI-powered tools without needing prior knowledge of their specifics. The server supports dynamic tool discovery, enabling clients to query the server for available tools and their functionalities. This modular and extensible design makes the MCP server ideal for a wide range of applications, from quick web searches to complex data analysis and visualization. The server's architecture ensures seamless interaction between clients and tools, maintaining conversational context and enabling natural language processing capabilities. With its resilient tool execution and intelligent routing, the MCP server enhances the efficiency and effectiveness of AI-driven tasks.

Features

  • Dynamic Tool Discovery: Clients can query the server to discover available tools and their functionalities.
  • Router-Agent Architecture: Utilizes a 'Router' LLM to intelligently select and execute the appropriate tool based on user requests.
  • Resilient Tools: Implements a 'Search-Evaluate-Refine' loop to ensure reliable and accurate results.
  • Contextual Prompting: Maintains conversational context for natural language processing and follow-up queries.
  • Modular and Extensible: Supports integration with various AI-powered tools and external APIs.

Usages

usage with vscode

{
  "mcpServers": {
    "my_tool": {
      "command": "python",
      "args": ["my_tool.py"]
    }
  }
}

usage with docker

{
  "mcpServers": {
    "my_tool": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "my_tool_image"
      ]
    }
  }
}

usage with sse

python
mcp.run(transport='sse', host="0.0.0.0", port=8000)

usage with streamable http

yaml
paths:
  /mcp:
    post:
      x-ms-agentic-protocol: mcp-streamable-1.0

usage with fastmcp

python
from mcp.server import FastMCP
app = FastMCP('demo')
@app.tool()
async def query(): ...

Tools

  1. quick_search

    Performs fast, real-time web searches for factual questions.

  2. medium_search

    Generates lists, recommendations, or comparisons from multiple web sources.

  3. conduct_deep_research

    Produces in-depth reports with deep analysis and citations.

  4. summarize_text

    Condenses long text into a specified summary format.

  5. visualize_data

    Creates data visualizations from JSON objects and saves them as images.

  6. explain_code

    Provides structured explanations of code snippets for non-technical audiences.