skills.rest
Nymbo
NymboCOMMUNITY
Explore all skills by this author →
𝕏@nymboqtGitHub

mcp-builder

Create MCP servers in Python and TypeScript with structured tools.

Updated Aug 27, 2026
Search Tags:#API integration#AI agent#MCP#SDK#server development#LLM tools#Model Context Protocol
One-click install
npx skills add https://github.com/Nymbo/Skills --skill mcp-builder-nymbo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/Nymbo/Skills/tree/main/mcp-builder
Command: npx skills add https://github.com/Nymbo/Skills --skill mcp-builder-nymbo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Developing high-quality Model Context Protocol (MCP) servers requires deep understanding of agent-centric design, protocol specifications, and best practices. This skill guides developers through the entire process, from planning to evaluation.

Core Features & Use Cases

  • Agent-Centric Design Principles: Learn to build tools optimized for LLM interaction, context limits, and actionable error messages, ensuring effective AI agent performance.
  • Comprehensive Workflow: Provides a structured approach covering deep research, implementation, code review, and rigorous evaluation phases for MCP server development.
  • Use Case: When integrating a new external API (like a CRM, project management tool, or internal service) with Claude, use this skill to ensure the MCP server is built to maximize AI agent effectiveness, reliability, and adherence to best practices.

Quick Start

Guide me through creating a new MCP server for the 'Jira' API, starting with understanding agent-centric design principles.

Frequently Asked Questions about mcp-builder

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

FAQPage Schema
How do I build an MCP server to connect Claude with external APIs?▼

MCP servers enable Claude to interact with external services through structured tools. Build them using FastMCP (Python) or the MCP SDK (Node/TypeScript), implementing tool registration with input validation schemas, multiple transport options (stdio, sse, http), and JSON/Markdown response formats to integrate APIs like CRM or project management systems.

What's the best way to design tools for LLM agents in an MCP server?▼

Agent-centric design prioritizes context efficiency and actionable error messages. Register tools with clear annotations, validate inputs against strict schemas, provide consistent JSON/Markdown responses, and ensure error messaging guides agents toward recovery—optimizing for how LLMs consume and act on information.

Can I use MCP servers with both Python and Node.js implementations?▼

Yes, MCP protocol supports multiple implementations. Use FastMCP for Python-based servers and the MCP SDK for Node/TypeScript. Both handle tool registration, schema validation, and transport protocols (stdio, sse, http) identically, letting you choose based on your existing stack.

What transport options does an MCP server support?▼

MCP servers support three transport mechanisms: stdio for local communication, sse (Server-Sent Events) for HTTP streaming, and http for REST-like access. Choose based on deployment context—stdio for CLI tools, sse/http for distributed or web-based agents.

How do I handle pagination and large datasets in MCP server tools?▼

Implement pagination at the tool level by defining input schemas with limit/offset or cursor parameters, validating bounds, and returning paginated results with metadata indicating total count and next-page availability—keeping responses manageable for agent context limits.

What security and deployment best practices apply to MCP servers?▼

Secure MCP servers by validating all tool inputs against schemas, implementing authentication where APIs require it, using secure transport (sse/http with TLS), handling errors without exposing sensitive data, and testing tools end-to-end before deployment to production agents.

Related Skills

Explore More Software Engineering Skills→
gotargotar
26

mcp-builder

Design MCP servers exposing documented tools for LLM API interactions.

Community
Advanced
juanlamadrid20juanlamadrid20

agent-mlops

Automate production deployment and monitoring of AI agents on Databricks.

Community
Advanced
ricardorochericardoroche
2

agent-orchestration-patterns

Standardize Pydantic tool schemas and agent state for multi-agent orchestration.

Community
Intermediate
InverterNetworkInverterNetwork

notion

Manage Notion pages, query databases, and deploy custom workers via CLI or REST API.

Official
Advanced