Mcp Builder

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.

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Process

🚀 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. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage.

Tool Naming and Discoverability: Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., github_create_issue, github_list_repos) and action-oriented naming.

Context Management: Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused, relevant data. Some clients support code execution which can help agents filter and process data efficiently.

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

1.2 Study MCP Protocol Documentation

Navigate the MCP specification:

Start with the sitemap to find relevant pages: https://modelcontextprotocol.io/sitemap.xml

Then fetch specific pages with .md suffix for markdown format (e.g., https://modelcontextprotocol.io/specification/draft.md).

Key pages to review:

• Specification overview and architecture
• Transport mechanisms (streamable HTTP, stdio)
• Tool, resource, and prompt definitions

1.3 Study Framework Documentation

Recommended stack:

• Language: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code, benefiting from its broad usage, static typing and good linting tools)
• Transport: Streamable HTTP for remote servers, using stateless JSON (simpler to scale and maintain, as opposed to stateful sessions and streaming responses). stdio for local servers.

Load framework documentation:

• MCP Best Practices: [📋 View Best Practices](./reference/mcpbestpractices.md) - Core guidelines

For TypeScript (recommended):

• TypeScript SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
• [⚡ TypeScript Guide](./reference/nodemcpserver.md) - TypeScript patterns and examples

For Python:

• Python SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
• [🐍 Python Guide](./reference/pythonmcpserver.md) - Python patterns and examples

1.4 Plan Your Implementation

Understand the API: Review the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed.

Tool Selection: Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.

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Phase 2: Implementation

2.1 Set Up Project Structure

See language-specific guides for project setup:

• [⚡ TypeScript Guide](./reference/nodemcpserver.md) - Project structure, package.json, tsconfig.json
• [🐍 Python Guide](./reference/pythonmcpserver.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
• Add examples in field descriptions

Output Schema:

• Define outputSchema where possible for structured data
• Use structuredContent in tool responses (TypeScript SDK feature)
• Helps clients understand and process tool outputs

Tool Description:

• Concise summary of functionality
• Parameter descriptions
• Return type schema

Implementation:

• Async/await for I/O operations
• Proper error handling with actionable messages
• Support pagination where applicable
• Return both text content and structured data when using modern SDKs

Annotations:

• readOnlyHint: true/false
• destructiveHint: true/false
• idempotentHint: true/false
• openWorldHint: true/false

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Phase 3: Review and Test

3.1 Code Quality

Review for:

• No duplicated code (DRY principle)
• Consistent error handling
• Full type coverage
• Clear tool descriptions

3.2 Build and Test

TypeScript:

• Run npm run build to verify compilation
• Test with MCP Inspector: npx @modelcontextprotocol/inspector

Python:

• Verify syntax: python -m py_compile your_server.py
• Test with MCP Inspector

See language-specific guides for detailed testing approaches and quality checklists.

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Phase 4: Register MCP Server (REQUIRED)

After building and testing, you MUST register the MCP server in the project configuration to make it available for use.

4.1 Generate Configuration Entry

Based on the tools you implemented, generate a configuration entry for config/mcp.json:

{
  "server-name": {
    "name": "Display Name",
    "description": "Description for Agent to understand when to use this server",
    "command": "node",
    "args": ["./server-directory/dist/index.js"],
    "env": {},
    "defaultEnabled": false,
    "tools": [
      {
        "name": "tool_name",
        "description": "Tool description",
        "inputSchema": {
          "type": "object",
          "properties": {
            "param1": {
              "type": "string",
              "description": "Parameter description"
            }
          },
          "required": ["param1"]
        }
      }
    ]
  }
}

4.2 Update config/mcp.json

1. Read the current config: Use read to read config/mcp.json
2. Add your server entry: Insert your server configuration into the mcpServers object
3. Write the updated config: Use bash with a heredoc to write the complete updated JSON

Example command to update config:

cat > config/mcp.json 
  
    Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat?
    3
  

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Reference Files

📚 Documentation Library

Load these resources as needed during development:

Skills API Integration (Load First for This Project)

• [🔌 Skills API Integration Guide](./reference/skills-api-integration.md) - Required for this project:
• config/mcp.json configuration format
• stdio transport requirement
• Environment variables setup
• Integration checklist
• Debugging tips

Core MCP Documentation

• MCP Protocol: Start with sitemap at https://modelcontextprotocol.io/sitemap.xml, then fetch specific pages with .md suffix
• [📋 MCP Best Practices](./reference/mcpbestpractices.md) - Universal MCP guidelines including:
• Server and tool naming conventions
• Response format guidelines (JSON vs Markdown)
• Pagination best practices
• Transport selection (streamable HTTP vs stdio)
• Security and error handling standards

SDK Documentation (Load During Phase 1/2)

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

Language-Specific Implementation Guides (Load During Phase 2)

• [🐍 Python Implementation Guide](./reference/pythonmcpserver.md) - Complete Python/FastMCP guide with:
• Server initialization patterns
• Pydantic model examples
• Tool registration with @mcp.tool
• Complete working examples
• Quality checklist

• [⚡ TypeScript Implementation Guide](./reference/nodemcpserver.md) - Complete TypeScript guide with:
• Project structure
• Zod schema patterns
• Tool registration with server.registerTool
• Complete working examples
• Quality checklist

Evaluation Guide (Load During Phase 4)

• [✅ Evaluation Guide](./reference/evaluation.md) - Complete evaluation creation guide with:
• Question creation guidelines
• Answer verification strategies
• XML format specifications
• Example questions and answers
• Running an evaluation with the provided scripts

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dp-archive
• Source: dp-archive/archive
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.