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building-mcp-servers

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).

231 stars

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/building-mcp-servers/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/asmayaseen/building-mcp-servers/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/building-mcp-servers/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How building-mcp-servers Compares

Feature / Agentbuilding-mcp-serversStandard Approach
Platform SupportmultiLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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).

Which AI agents support this skill?

This skill is compatible with multi.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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:**
- [MCP Best Practices](references/mcp_best_practices.md) - Core guidelines
- [TypeScript Guide](references/node_mcp_server.md) - TypeScript patterns
- [Python Guide](references/python_mcp_server.md) - Python/FastMCP patterns

**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:
- [TypeScript Guide](references/node_mcp_server.md) - Project structure, package.json, tsconfig.json
- [Python Guide](references/python_mcp_server.md) - Module organization, dependencies

#### 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:**
```bash
npm run build
npx @modelcontextprotocol/inspector
```

**Python:**
```bash
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:**
```xml
<evaluation>
  <qa_pair>
    <question>Your question here</question>
    <answer>Expected answer</answer>
  </qa_pair>
</evaluation>
```

See [Evaluation Guide](references/evaluation.md) for complete guidelines.

---

## Docker/Containerization

### Transport Security (allowed_hosts)

FastMCP validates Host headers. For Docker, configure:

```python
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

- [MCP Best Practices](references/mcp_best_practices.md) - Universal guidelines
- [Python Guide](references/python_mcp_server.md) - Python/FastMCP patterns
- [TypeScript Guide](references/node_mcp_server.md) - TypeScript patterns
- [TaskFlow Patterns](references/taskflow_patterns.md) - Internal server patterns
- [Evaluation Guide](references/evaluation.md) - Creating evaluations