ai-agent-tool-builder

Create MCP servers and function-calling tools for AI agents.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/doctorduke/claude-config --skill ai-agent-tool-builder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-agent-tool-builder
Source: https://github.com/doctorduke/claude-config/tree/main/skills/ai-agent-tool-builder
Command: npx skills add https://github.com/doctorduke/claude-config --skill ai-agent-tool-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables you to design and implement MCP-based tools, servers, and tool chains that empower AI agents to perform deterministic, safe actions with standardized interfaces.

Core Features & Use Cases

  • Create MCP servers (Python FastMCP, TypeScript SDK) and expose tools via decorators or explicit registrations.
  • Design JSON-based function calling schemas and tool composition patterns for scalable agent workflows.
  • Provide templates and patterns for tool testing, security best practices, and integration with agents.

Quick Start

Define a minimal MCP tool on Python using FastMCP, decorate a function with @mcp.tool(), and outline a simple client call to exercise the tool end-to-end.

Frequently Asked Questions about ai-agent-tool-builder

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

FAQPage Schema
How do I create an MCP server for AI agents?▼

Create an MCP server by defining tool functions with type hints, decorating them with @mcp.tool() in FastMCP (Python) or using the TypeScript SDK, then registering them to expose a standardized interface. This lets agents call your tools deterministically with validated JSON schemas.

What's the difference between function-calling tools and MCP servers?▼

Function-calling tools are individual schemas that define agent-callable functions; MCP servers are standardized containers that host multiple tools with transport protocols and lifecycle management. MCP servers provide robust error handling, security boundaries, and composability across agent systems.

Can I wrap existing CLI tools or APIs as MCP-based tools?▼

Yes. Design a tool function that invokes your CLI or API, add type hints and JSON Schema validation, decorate it with @mcp.tool(), and register it in your MCP server. This pattern lets agents safely trigger external systems with predictable, testable interfaces.

How do I compose multiple tools into agent workflows?▼

Define tool composition patterns in your MCP framework by chaining function outputs as inputs to subsequent tools, using explicit schema dependencies and error handling. This enables multi-step workflows where agents orchestrate tool sequences safely.

What security practices should I follow when building agent tools?▼

Implement input validation via JSON Schema, use type hints to enforce contracts, handle errors robustly without exposing internals, and design tools with principle of least privilege. Test integration patterns end-to-end before deployment to agents.

Do I need to write custom schemas for each tool?▼

No. Type-hinted function signatures in FastMCP and TypeScript SDK auto-generate JSON schemas. You define parameters with Python or TypeScript types; the framework validates and serializes them, reducing manual schema maintenance.