skills.rest
omeraltn
omeraltnCOMMUNITY
Explore all skills by this author →
GitHub

agency-mcp-builder

Build MCP servers exposing typed tools and resources to AI agents.

Updated Apr 11, 2026
Search Tags:#pydantic#mcp#tool-design#mcp-server#agent-integration#typed-parameters#mcp-sdk
One-click install
npx skills add https://github.com/omeraltn/ice_cream_website_testing --skill agency-mcp-builder-omeraltn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agency-mcp-builder
Source: https://github.com/omeraltn/ice_cream_website_testing/tree/main/.antigravity/agency-mcp-builder
Command: npx skills add https://github.com/omeraltn/ice_cream_website_testing --skill agency-mcp-builder-omeraltn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the ambiguity and friction that cause AI agents to pick the wrong tool or send malformed parameters by guiding developers to design, build, and test Model Context Protocol servers that are agent-friendly, typed, and production-ready.

Core Features & Use Cases

  • Agent-friendly tool interfaces: Name tools descriptively, write usage-focused descriptions, and define typed parameter schemas so agents know exactly when and how to call a tool.
  • Production-quality servers: Implement input validation, structured JSON outputs, robust error handling with isError semantics, secure environment-based secrets, and transport choices (stdio, SSE, HTTP).
  • Testing and iteration: Test the full agent loop (discovery → call → response) to catch misnaming, bad params, and error paths; use examples like a tickets search tool or GitHub issue search to validate behavior.

Quick Start

Use the MCP Builder workflow: define clear verb_noun tool names, write one-sentence descriptions that state when to use each tool, declare typed params with Zod or Pydantic, implement error-handled handlers returning structured results, and run the server locally with a stdio transport to test real agent calls.

Frequently Asked Questions about agency-mcp-builder

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

FAQPage Schema
How do I build an MCP server that AI agents can call reliably without malformed parameters?▼

To build a reliable MCP server, define descriptive tool names, write usage-focused descriptions, and declare typed parameter schemas using Pydantic or Zod. This approach enforces input validation and structured JSON outputs, ensuring agents call tools correctly and receive robust error signaling.

What's the best way to design MCP tools for AI agent integration?▼

The best way to design MCP tools for AI agent integration is to use clear verb_noun naming, provide one-sentence descriptions stating when to use each tool, and implement typed parameters. Structured results and graceful error handling with isError semantics ensure agents understand tool responses.

Can I use typed parameters and Pydantic validation with an MCP server?▼

Yes, you can use Pydantic for typed parameter validation in an MCP server. Defining typed schemas ensures that AI agents send correctly formatted parameters, reducing ambiguity and preventing malformed tool calls during discovery and execution loops.

Does the MCP SDK support multiple transports like stdio, SSE, and HTTP?▼

The MCP SDK supports stdio, SSE, and HTTP transports. You can run your server locally with a stdio transport to test real agent calls, or deploy it using SSE and HTTP to expose tools and resources for production agent integration.

How do I test an MCP server to catch bad parameters and error paths?▼

Test an MCP server by running the full agent loop from discovery to call to response. This workflow catches misnaming, bad parameters, and error paths by validating behavior against examples like a tickets search tool or GitHub issue search before production deployment.

Why does my AI agent pick the wrong tool or send malformed parameters during MCP calls?▼

AI agents pick the wrong tool or send malformed parameters due to ambiguous tool interfaces. Eliminate this friction by naming tools descriptively, writing usage-focused descriptions, and declaring typed parameter schemas so agents know exactly when and how to call each tool.

Related Skills

Explore More Software Engineering Skills→
brunogamabrunogama

optimize-schema

Optimize MCP tool schemas with detail-level enums and token-savings estimates.

Community
Advanced
dieu-donneedieu-donnee

mcp-developer

Scaffold MCP servers and clients in TypeScript or Python.

Community
Advanced
Sir-chawakornSir-chawakorn
9

structured-output-llm

Enforce machine-parseable JSON output from LLMs using provider-native structured output modes.

Community
Advanced
MassimilianoPiliMassimilianoPili

spring-ai-mcp-server-patterns

Build MCP servers with Spring AI using Java annotations and prompt templates.

Community
Advanced