---

name: mcp-builder description: Build, test, and deploy production-quality MCP (Model Context Protocol) servers. Full lifecycle -- build locally, test interactively with MCP Inspector, iterate until working, then optionally deploy to Runlayer. Triggers include "build an MCP server", "create MCP tools", "implement MCP", "add tools for [service]", "connect [service] via MCP", or any MCP server development task.

MCP Server Builder

Build, test, and deploy MCP servers that enable LLMs to accomplish real-world tasks through well-designed tools. Server quality is measured by how effectively agents can use the tools, not by API coverage alone.

Before You Start

Ask the user which programming language they want to use before doing anything else. Use AskQuestion to prompt for their preferred language. Do not proceed until they answer. Then load the corresponding language-specific reference from references/:

• TypeScript -> read references/typescript.md
• Python -> read references/python.md
• Other -> use the language-agnostic patterns in references/guidelines.md and adapt to the chosen language

Apply the language-specific conventions (naming, SDK, validation library, project structure, idioms) from the loaded reference throughout the entire build.

Development Process

Follow six phases in order. Phases 1-5 are mandatory. Do NOT skip any phase. Do NOT declare the server complete until Phase 5 passes.

1. Research & Plan -- Study the target API (endpoints, auth, data models, rate limits, pagination). Plan tool coverage. Prioritize comprehensive API coverage over workflow shortcuts when uncertain.
2. Implement -- Build infrastructure first (API client, auth, errors, formatters), then tools incrementally.
3. Review & Build -- Build, lint, fix all errors. Review for duplication, consistent errors, type coverage.
4. Evaluate -- Create 10 complex realistic questions requiring multiple tool calls with stable verifiable answers.
5. Verify (MANDATORY) -- Test the server by sending real MCP protocol messages. At minimum: initialize, tools/list, and one tool call. Iterate on failures. The server is NOT done until this passes. See references/testing.md.
6. Deploy (Optional) -- When tests pass, offer to deploy to Runlayer via uvx runlayer deploy. See references/deploy.md.

Architecture Decisions

Determine these upfront:

Transport:

• stdio -- local/CLI tools, single user, simple setup
• Streamable HTTP -- remote/cloud, multi-client, serverless. Avoid SSE (deprecated).

State:

• Stateless (recommended for remote) -- fresh server per request, horizontal scaling, no session leaks
• Stateful -- when session continuity is required

Auth:

• OAuth 2.1 -- remote servers with user identity
• API key via env vars -- simpler integrations
• None -- local-only tools

Language conventions (applied after user selects language):

Language	Server name	Validation	SDK
TypeScript	{service}-mcp-server	Zod	@modelcontextprotocol/sdk
Python	{service}_mcp	Pydantic	mcp (FastMCP)
Other	{service}-mcp	JSON Schema	Implement protocol directly

Tool Design Rules

1. Name: {service}_{action}_{resource} in snake_case. Always prefix with service name.
2. Required fields: name, title, description, inputSchema, outputSchema, annotations
3. Annotations: Set readOnlyHint, destructiveHint, idempotentHint, openWorldHint on every tool.
4. Descriptions: Must precisely match functionality. Include parameter docs, return schema, usage examples (when to use AND when NOT to use), and error conditions.
5. Schemas: Use strict mode. Add constraints (min/max/pattern/enum) and .describe() on every field.
6. Responses: Return both content (text) and structuredContent (typed data).

For detailed patterns, validation examples, response formatting, error handling, pagination, auth, testing, security, logging, and deployment guidance, read references/guidelines.md.

Project Structure

{service}-mcp-server/
├── src/
│   ├── index.ts          # Entry point (dual-mode: local + serverless)
│   ├── tools/            # Tool implementations + registry
│   ├── resources/        # MCP resource handlers (if needed)
│   ├── services/         # API clients, external service wrappers
│   ├── schemas/          # Validation schemas
│   ├── utils/            # Shared helpers (retry, formatting, errors)
│   ├── auth/             # Auth, token storage, middleware
│   └── types/            # Type definitions
├── tests/
│   ├── unit/
│   └── integration/
└── README.md

Implementation Workflow

1. Scaffold the project structure above
2. Build shared utilities first:
   • makeApiRequest -- centralized HTTP client with auth, timeout, retries
   • handleApiError -- maps HTTP status to MCP error codes with context
   • createToolResponse / createErrorResponse -- consistent response envelopes
   • withRetry -- exponential backoff (retry 429, 5xx, network errors; skip other 4xx)
3. Implement tools one at a time, each with:
   • Schema-validated input (strict mode, constraints, descriptions)
   • Handler wrapped in withRetry
   • Both text + structured content in response
   • Pagination for any list operation (default 20-50 items, return has_more + next_offset)
   • Character limit enforcement (truncate at ~25K chars with message)
4. Implement resources if the service has URI-addressable data
5. Set up transport (stdio or Streamable HTTP based on deployment target)
6. Add dual-mode entry point (serverless handler export + local server start)
7. Test -- unit tests for handlers/validators, integration tests gated on env vars
8. Evaluate -- 10 QA pairs per the evaluation format

Quality Checklist

Before declaring the server complete:

• Every tool has name, title, description, inputSchema, outputSchema, annotations
• All annotations correctly set (readOnlyHint, destructiveHint, idempotentHint, openWorldHint)
• All inputs schema-validated with constraints and descriptions
• Error messages are actionable and safe (no internal details)
• Retry with exponential backoff on transient failures
• All list tools support pagination with has_more / next_offset
• Character limit enforced with truncation message
• No duplicated code -- shared utilities extracted
• Strict typing throughout, no any types
• Async/await for all I/O
• Auth validated on every request
• No secrets in logs
• Build completes without errors

Phase 5: Verify (MANDATORY -- DO NOT SKIP)

This phase is required. The server is NOT complete until these tests pass. Read references/testing.md for detailed patterns.

Minimum Viable Test (always run, no credentials needed)

Even without API credentials, you MUST verify the server works at the MCP protocol level by piping JSON-RPC messages to the server process:

1. Initialize -- Send initialize request, confirm server responds with protocolVersion, capabilities, and serverInfo.
2. List tools -- Send tools/list, confirm all expected tools appear with correct names, schemas, and annotations.
3. Error handling -- Call a tool (e.g. a read tool) and verify it returns a graceful isError: true response (not a crash) when credentials are missing or the resource doesn't exist.

Example for stdio servers:

# Test 1: Initialize
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}' | node dist/index.js 2>/dev/null

# Test 2: List tools (send initialize + notification + list in sequence)
printf '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}\n{"jsonrpc":"2.0","method":"notifications/initialized"}\n{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}\n' | node dist/index.js 2>/dev/null

# Test 3: Call a tool and verify error handling
printf '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}\n{"jsonrpc":"2.0","method":"notifications/initialized"}\n{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"SERVICE_TOOL_NAME","arguments":{}}}\n' | node dist/index.js 2>/dev/null

Adapt node dist/index.js for Python (uv run server.py) as needed.

If any test fails, fix the code and re-run. Do not proceed to Phase 6.

Full Interactive Testing (when credentials are available)

1. Start MCP Inspector:

   • TypeScript: npx @anthropic-ai/mcp-inspector npx tsx src/index.ts
   • Python: npx @anthropic-ai/mcp-inspector uv run server.py

2. Test each tool systematically:

   • Start with read-only tools (lowest risk)
   • Test edge cases: empty inputs, pagination boundaries, max lengths
   • Verify error handling with invalid inputs
   • Test destructive operations last (with user confirmation)

3. On failure:

   • Capture the error message from Inspector output
   • Fix the issue in code
   • Re-run the failing test
   • Continue until all tools pass

4. Exit criteria: All tools respond correctly to valid inputs and return proper MCP errors for invalid inputs.

Phase 6: Deploy (Optional)

When all interactive tests pass, ask the user:

"Your MCP server is working correctly. Would you like to deploy it to Runlayer?"

If yes, read references/deploy.md for full details, then:

Step 1: Collect credentials

Use AskQuestion to prompt for both values. Do not guess or skip.

1. Personal API Key — "Go to your Runlayer dashboard → Settings → API Keys → Personal API Key. Paste it here."
2. Runlayer tenant URL — "What is your Runlayer tenant URL? (e.g. https://mycompany.runlayer.com or https://ecs.prod.runlayer.com)"

Step 2: Ensure Streamable HTTP transport

If the server was built with stdio-only transport, add dual-mode support before deploying:

• Add express dependency
• Add /health endpoint (returns {"status":"ok"})
• Add /mcp POST endpoint with StreamableHTTPServerTransport
• Use PORT env var to switch: stdio when no PORT, HTTP when PORT is set
• Rebuild, test health + MCP endpoints locally with curl

Step 3: Create deployment files

1. Dockerfile — Use template from references/deploy.md. Test locally with docker build . before proceeding.
2. .dockerignore — Exclude node_modules, dist, .git, .env, tests
3. Initialize deployment to get an ID:

   uvx runlayer deploy init --secret <API_KEY> --host <TENANT_URL>
   

   This generates runlayer.yaml with the deployment ID. Edit it to set service.port to match your server.

Step 4: Deploy

uvx runlayer deploy --secret <API_KEY> --host <TENANT_URL>

Step 5: Report success

Show:

• Deployment ID
• MCP proxy URL: https://<tenant>/api/v1/proxy/<deployment-id>/mcp
• Cursor MCP config snippet for connecting to the deployed server
• Any env vars the user still needs to configure in Runlayer (API keys, credentials for the target service)