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mcp

Model, implement, and evaluate MCP servers in Python or TypeScript.

Updated Jan 28, 2026
Search Tags:#typescript#python#sdk#mcp#server#tools#gemini-cli
One-click install
npx skills add https://github.com/brixtonpham/claude-config --skill mcp-brixtonpham
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp
Source: https://github.com/brixtonpham/claude-config/tree/main/skills/mcp
Command: npx skills add https://github.com/brixtonpham/claude-config --skill mcp-brixtonpham

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @modelcontextprotocol/sdk, anthropic, mcp, and includes scripts (resource) and references (resource) components.

What problem does it solve?

MCP servers are essential to provide AI agents with controlled, queryable access to tools, prompts, and resources. This Skill defines how to design, implement, and evaluate Model Context Protocol servers in Python and TypeScript, enabling robust tool orchestration and experimentation.

Core Features & Use Cases

  • Build and register MCP tools with strict input validation and clear annotations
  • Set up Python (FastMCP) or TypeScript (MCP SDK) servers with robust error handling and pagination
  • Use Gemini CLI and evaluation harness to discover, run, and assess tools across multiple servers
  • Create reusable utilities, resources, and prompts to support scalable AI agent workflows

Quick Start

Start by wiring up a sample MCP server (Python or TypeScript), register a couple of tools, connect via stdio transport, and run a basic tool invocation.

Frequently Asked Questions about mcp

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

FAQPage Schema
How do I build an MCP server for AI tools?▼

To build an MCP server, use the Python FastMCP or TypeScript MCP SDK to register tools with strict input validation, set up robust error handling and pagination, and connect via stdio transport for tool invocation.

Can I use Python FastMCP and TypeScript MCP SDK together?▼

Yes, you can implement MCP servers using either Python FastMCP or TypeScript MCP SDK, and use Gemini CLI to discover, run, and assess tools across multiple servers simultaneously.

What is the best way to design MCP tools for AI agents?▼

The best way to design MCP tools is to enforce strict input validation, apply clear annotations, and implement robust error handling and pagination to support scalable AI agent workflows.

How do I evaluate and discover tools across multiple MCP servers?▼

You can evaluate and discover tools across multiple MCP servers by using Gemini CLI and an evaluation harness to run tool invocations and assess their performance and integration.

Does this MCP server implementation require specific security best practices?▼

Yes, implementing MCP servers requires enforcing validation, pagination, error handling, and security best practices to ensure controlled, queryable access to tools, prompts, and resources for AI agents.

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