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).
What this skill does
# 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 evaluationsRelated in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.