SKILL.md
MCP Server Development Guide
Degree of freedom: MIXED. Which tools to expose [HIGH freedom]; SDK tool shape, annotations, env-based auth, and Inspector [LOW freedom — run exactly].
Create MCP servers that enable LLMs to interact with external services.
How to reason
- Observe — the real agent job (search, create, send), not the raw API catalog
- Interpret — compose-from-primitives vs one workflow tool
- Classify — prefixed action names, typed params, annotations (readOnly / destructive / idempotent)
- Verify — Inspector + valid/invalid/edge; errors tell the agent what to do next
Worked example
Observe: "give Claude access to Linear"; agents will search issues and file one — not walk the full GraphQL schema.
Interpret: start with workflow-shaped tools, not 40 raw endpoints.
Classify:linearsearchissues,linearcreateissue; zod params;createisreadOnlyHint: false.
Ship: envLINEARAPIKEY(throw if missing); Inspector on both tools; no hardcoded token.
Self-critique before reporting
- Names — action-oriented and service-prefixed
- Errors actionable — next step in the message; no hardcoded credentials
- Annotations match — destructive tools say so
- Right owner — pack SKILL.md authoring →
meta-skill-creator; calling an existing MCP is not this skill
Overview
MCP (Model Context Protocol) servers expose tools that AI agents can use. Quality is measured by how well they enable agents to accomplish real tasks.
Quick Start [HIGH freedom]
1. Choose Stack
Recommended: TypeScript with MCP SDK
- High-quality SDK support
- Good compatibility across environments
- Strong type safety
Alternative: Python with FastMCP
- Good for Python-heavy workflows
2. Project Structure
my-mcp-server/
├── src/
│ ├── index.ts # Entry point
│ ├── tools/ # Tool implementations
│ │ ├── search.ts
│ │ └── create.ts
│ └── utils/ # Shared utilities
│ ├── api-client.ts
│ └── error-handler.ts
├── package.json
├── tsconfig.json
└── README.md
Tool Design Principles [HIGH freedom]
1. Clear Naming
// ✅ Good - action-oriented, prefixed
'github_create_issue'
'github_list_repos'
'slack_send_message'
// ❌ Avoid - vague
'process'
'handle'
'do_thing'
2. Concise Descriptions
{
name: 'github_search_issues',
description: 'Search GitHub issues by query, state, and labels. Returns issue title, number, and URL.',
}
3. Typed Parameters
import { z } from 'zod';
const searchIssuesSchema = z.object({
query: z.string().describe('Search query string'),
state: z.enum(['open', 'closed', 'all']).default('open'),
labels: z.array(z.string()).optional().describe('Filter by labels'),
limit: z.number().min(1).max(100).default(10),
});
4. Actionable Errors
// ❌ Bad
throw new Error('Failed');
// ✅ Good
throw new Error(
`GitHub API rate limit exceeded. ` +
`Resets at ${resetTime}. ` +
`Try again later or authenticate for higher limits.`
);
Implementation Pattern [LOW freedom — this shape]
Basic Tool Structure
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { z } from 'zod';
const server = new McpServer({
name: 'my-service',
version: '1.0.0',
});
// Define tool
server.tool(
'service_action',
'Description of what this tool does and when to use it',
{
param1: z.string().describe('What this param is for'),
param2: z.number().optional().describe('Optional param'),
},
async ({ param1, param2 }) => {
// Implementation
const result = await performAction(param1, param2);
return {
content: [
{
type: 'text',
text: JSON.stringify(result, null, 2),
},
],
};
}
);
Tool Annotations
server.tool(
'delete_item',
'Delete an item permanently',
{ id: z.string() },
async ({ id }) => { /* ... */ },
{
annotations: {
readOnlyHint: false, // Modifies data
destructiveHint: true, // Cannot be undone
idempotentHint: true, // Safe to retry
openWorldHint: false, // Closed set of operations
},
}
);
Best Practices [HIGH freedom]
API Coverage vs Workflow Tools
| Approach | When to Use |
|---|---|
| full API coverage | Agent needs flexibility to compose operations |
| Workflow tools | Specific task needs multi-step automation |
Default: Start with full API coverage, add workflow tools for common patterns.
Response Formatting
// Return structured data
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
data: results,
metadata: { count: results.length },
}, null, 2),
}],
};
Pagination Support
const listItemsSchema = z.object({
limit: z.number().min(1).max(100).default(20),
cursor: z.string().optional().describe('Pagination cursor from previous response'),
});
// Return cursor in response
return {
items: results,
nextCursor: hasMore ? lastId : null,
};
Testing [LOW freedom — run exactly]
1. Build Check
npm run build # Must pass without errors
2. Test with Inspector
npx @modelcontextprotocol/inspector
3. Test Each Tool
- Valid inputs → expected output
- Invalid inputs → helpful error
- Edge cases → graceful handling
Quality Checklist [LOW freedom — do not skip]
- All tools have clear, descriptive names
- All parameters have descriptions
- Error messages are actionable
- Pagination for list operations
- No hardcoded credentials
- TypeScript types for all inputs/outputs
- README documents all tools
- Examples provided for complex tools
Common Patterns [HIGH freedom]
Authentication
const apiKey = process.env.SERVICE_API_KEY;
if (!apiKey) {
throw new Error('SERVICE_API_KEY environment variable required');
}
Rate Limiting
import { RateLimiter } from 'limiter';
const limiter = new RateLimiter({
tokensPerInterval: 100,
interval: 'minute',
});
async function callApi() {
await limiter.removeTokens(1);
// Make API call
}
Caching
const cache = new Map<string, { data: any; expiry: number }>();
async function getCached(key: string, fetcher: () => Promise<any>) {
const cached = cache.get(key);
if (cached && cached.expiry > Date.now()) {
return cached.data;
}
const data = await fetcher();
cache.set(key, { data, expiry: Date.now() + 60000 });
return data;
}
Resources
- MCP Specification: https://modelcontextprotocol.io
- TypeScript SDK: https://github.com/modelcontextprotocol/typescript-sdk
- Python SDK: https://github.com/modelcontextprotocol/python-sdk