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Building mcp servers

Skill mjunaidca/mjs-agent-skills/.claude/skills/building-mcp-servers

A curated collection of Agent Skills — reusable units of intelligence that teach AI General Agents how to perform specific tasks autonomously.

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Guides creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK). Covers tool design, authentication, Docker deployment, and evaluation creation. NOT when consuming existing MCP servers (use the server directly).

SKILL.md

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MCP Server Development Guide

Overview

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.


High-Level Workflow

Creating a high-quality MCP server involves four main phases:

Phase 1: Deep Research and Planning

1.1 Understand Modern MCP Design

API Coverage vs. Workflow Tools: Balance comprehensive API endpoint coverage with specialized workflow tools. When uncertain, prioritize comprehensive API coverage.

Tool Naming and Discoverability: Use consistent prefixes (e.g., github_create_issue, github_list_repos) and action-oriented naming.

Context Management: Design tools that return focused, relevant data. Support filtering/pagination.

Actionable Error Messages: Error messages should guide agents toward solutions with specific suggestions.

1.2 Study MCP Protocol Documentation

Start with the sitemap: https://modelcontextprotocol.io/sitemap.xml

Fetch pages with .md suffix (e.g., https://modelcontextprotocol.io/specification/draft.md).

Key pages: Specification overview, transport mechanisms, tool/resource/prompt definitions.

1.3 Study Framework Documentation

Recommended stack:

  • Language: TypeScript (high-quality SDK, good AI code generation)
  • Transport: Streamable HTTP for remote servers, stdio for local servers

Load framework documentation:

SDK Documentation:

  • TypeScript: https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
  • Python: https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md

1.4 Plan Your Implementation

Review the service's API documentation. List endpoints to implement, starting with most common operations.


Phase 2: Implementation

2.1 Set Up Project Structure

See language-specific guides:

2.2 Implement Core Infrastructure

Create shared utilities:

  • API client with authentication
  • Error handling helpers
  • Response formatting (JSON/Markdown)
  • Pagination support

2.3 Implement Tools

For each tool:

Input Schema:

  • Use Zod (TypeScript) or Pydantic (Python)
  • Include constraints and clear descriptions

Output Schema:

  • Define outputSchema where possible
  • Use structuredContent in responses

Tool Description:

  • Concise summary, parameter descriptions, return type

Annotations:

  • readOnlyHint, destructiveHint, idempotentHint, openWorldHint

Phase 3: Review and Test

3.1 Code Quality

Review for: DRY principle, consistent error handling, full type coverage, clear descriptions.

3.2 Build and Test

TypeScript:

npm run build
npx @modelcontextprotocol/inspector

Python:

python -m py_compile your_server.py
# Test with MCP Inspector

Phase 4: Create Evaluations

Create 10 evaluation questions to test LLM effectiveness with your server.

Requirements for each question:

  • Independent, read-only, complex, realistic, verifiable, stable

Output Format:

<evaluation>
  <qa_pair>
    <question>Your question here</question>
    <answer>Expected answer</answer>
  </qa_pair>
</evaluation>

See Evaluation Guide for complete guidelines.


Docker/Containerization

Transport Security (allowed_hosts)

FastMCP validates Host headers. For Docker, configure:

from mcp.server.fastmcp import FastMCP
from mcp.server.transport_security import TransportSecuritySettings

transport_security = TransportSecuritySettings(
    allowed_hosts=[
        "127.0.0.1:*", "localhost:*", "[::1]:*",
        "mcp-server:*",  # Docker container name
        "0.0.0.0:*",
    ],
)
mcp = FastMCP("my_server", transport_security=transport_security)

Health Check Endpoint

Add /health endpoint via middleware (see references for full example).


Verification

Run: python3 scripts/verify.py

Expected: ✓ building-mcp-servers skill ready

If Verification Fails

  1. Run diagnostic: Check references/ folder exists
  2. Check: All reference files present
  3. Stop and report if still failing

References

Gives 6 of the 12 instructions most mcp tooling skills give in ~1.1k tokens

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-06

  • create ten complex read-only evaluation questionshere, and in 71 of 638, across 17 files
  • test servers using MCP Inspectorhere, and in 60 of 638, across 18 files
  • provide actionable error messageshere, and in 56 of 638, across 14 files
  • prioritize comprehensive API coverage over specific workflowshere, and in 54 of 638, across 12 files
  • use TypeScript and Streamable HTTP for remote serversin 53 of 638, across 7 files
  • define structured output schemas where possiblehere, and in 51 of 638, across 9 files
  • use Zod or Pydantic for input schemashere, and in 48 of 638, across 6 files
  • fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • load framework documentation using WebFetchin 45 of 638, across 3 files
  • verify each evaluation answer independentlyin 45 of 638, across 3 files
  • implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 28 of 638, across 10 files

Said here and by no other author read

  • use consistent prefixes for tool naming
  • load relevant framework documentation
  • list endpoints to implement

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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