mcp-builder

Create MCP servers for LLM interaction with external services.

7|5|Updated Sep 12, 2025

Search Tags:#ai agents #evaluation #api integration #mcp server #tool development #distributed ai #server configuration

Ready to run

npx skills add https://github.com/Cam10001110101/claude-skills-base --skill mcp-builder-cam10001110101

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/Cam10001110101/claude-skills-base/tree/main/mnt/skills/examples/mcp-builder
Command: npx skills add https://github.com/Cam10001110101/claude-skills-base --skill mcp-builder-cam10001110101

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, uvicorn, websockets, asyncio, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Setting up and configuring Multi-Agent Communication Protocol (MCP) servers for distributed AI systems can be a complex and manual process, hindering rapid prototyping and deployment.

Core Features & Use Cases

MCP Server Creation: Facilitate the creation and configuration of Multi-Agent Communication Protocol (MCP) servers.
Rapid Deployment: Enable rapid deployment of multi-agent systems by automating server setup.
Agent Configuration: Define agent roles, permissions, and communication channels within the server.
Use Case: Quickly deploy a new MCP server instance for a research project, allowing multiple AI agents to communicate and collaborate on a shared task, accelerating experimental setups.

Quick Start

This skill helps build and configure MCP servers.

To create a basic MCP server configuration (conceptual):

from scripts.connections import create_mcp_server_config

config = create_mcp_server_config(

port=8081,

agents=["data_collector_agent", "analyzer_agent"],

security_level="high"

)

print(f"Generated server configuration: {config}")

# To evaluate a server configuration for best practices (conceptual):

# from scripts.evaluation import run_server_evaluation

# run_server_evaluation("my_server_config.json")

For detailed instructions, refer to the 'reference/' directory.

Frequently Asked Questions about mcp-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create an MCP server to connect LLMs with external services?▼

MCP servers enable LLMs to interact with external services through standardized protocols. This Skill provides templates and best practices for building MCP servers using FastAPI or Node/TypeScript MCP SDK, handling tool registration, input validation with Pydantic or Zod, and supporting stdio, SSE, and HTTP transports for distributed AI systems.

What's the best way to design tools for an MCP server with proper validation and error handling?▼

Tool design for MCP servers requires explicit metadata registration, strict input validation using Pydantic or Zod, consistent naming conventions, and comprehensive error handling. This Skill covers planning tool interfaces, configurable response formats, pagination support, and character limits to meet MCP best practices across Python and TypeScript implementations.

Can I use FastAPI and asyncio to build a production-ready MCP server?▼

Yes. FastAPI with asyncio, uvicorn, and websockets provides a robust foundation for MCP servers. This Skill guides server configuration, secure transport setup, agent role definition, and deployment automation so you can quickly launch multi-agent systems with proper security levels and communication channels.

How do I evaluate an MCP server configuration for best practices compliance?▼

This Skill includes evaluation scripts that assess MCP server configurations against best practices for tool registration, input validation strictness, transport protocol support, and security implementation. Evaluation helps verify your server meets MCP standards before deploying to production environments.

What transports does an MCP server need to support for distributed AI agents?▼

MCP servers should support multiple transports—stdio for local agent communication, SSE for server-sent events, and HTTP for REST-based integration. This Skill documents transport configuration for each implementation language, enabling flexible deployment across different agent architectures and network topologies.