MCP Builder

JetBrainsOfficialCreated by AnthropicDistributed by JetBrains

Skill
Guidelines
Agent tooling
Apache-2.0
Low risk
Has scripts

From the author

Guide for creating 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).

— the description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This guide provides a structured four-phase workflow for building MCP servers that enable LLMs to interact with external services. It covers the entire lifecycle from initial research and planning to implementation using Python or TypeScript SDKs, and concludes with a rigorous evaluation process to ensure tool effectiveness and reliability.

What it does

Outlines best practices for MCP server design, including tool naming and context management.
Provides a structured development process for building servers in Python or TypeScript.
Offers guidance on implementing robust tool schemas using Zod or Pydantic.
Details how to create and run evaluations to verify LLM tool usage.
Includes references for core MCP protocol documentation and SDK-specific implementation patterns.

When to use it

When you need to integrate external APIs or services into an LLM-based agent.
When building new MCP servers to expand an agent's capabilities.
When you need to ensure your MCP server follows standardized design and quality patterns.
When you are ready to test and validate the effectiveness of your implemented tools.

Advantages

Promotes consistent and discoverable tool design for better LLM performance.
Supports both Python and TypeScript development environments.
Includes a standardized evaluation methodology to ensure high-quality, reliable tool interactions.
Provides clear, actionable steps for error handling and API integration.

Usage examples

“How should I structure my MCP server project in TypeScript?”: The agent provides the project structure, package.json, and tsconfig.json requirements as outlined in the TypeScript implementation guide.
“What are the best practices for naming tools in an MCP server?”: The agent explains the importance of clear, descriptive, and action-oriented naming conventions like 'github_create_issue' to improve tool discoverability.
“How do I verify that my MCP server is working correctly?”: The agent guides you through using the MCP Inspector and creating a set of 10 complex, realistic evaluation questions to test the server.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/jetbrains/mcp-builder/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip mcp-builder.zip -d .agents/skills

Documentation by Anthropic

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.

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 - Core guidelines

For TypeScript (recommended):

TypeScript SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
⚡ TypeScript Guide - TypeScript patterns and examples

For Python:

Python SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
🐍 Python Guide - 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.

Phase 2: Implementation

2.1 Set Up Project Structure

See language-specific guides for project setup:

⚡ TypeScript Guide - Project structure, package.json, tsconfig.json
🐍 Python Guide - 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

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.

Phase 4: Create Evaluations

After implementing your MCP server, create comprehensive evaluations to test its effectiveness.

Load ✅ Evaluation Guide for complete evaluation guidelines.

4.1 Understand Evaluation Purpose

Use evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions.

4.2 Create 10 Evaluation Questions

To create effective evaluations, follow the process outlined in the evaluation guide:

Tool Inspection: List available tools and understand their capabilities
Content Exploration: Use READ-ONLY operations to explore available data
Question Generation: Create 10 complex, realistic questions
Answer Verification: Solve each question yourself to verify answers

4.3 Evaluation Requirements

Ensure each question is:

Independent: Not dependent on other questions
Read-only: Only non-destructive operations required
Complex: Requiring multiple tool calls and deep exploration
Realistic: Based on real use cases humans would care about
Verifiable: Single, clear answer that can be verified by string comparison
Stable: Answer won't change over time

4.4 Output Format

Create an XML file with this structure:

<evaluation>
  <qa_pair>
    <question>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?</question>
    <answer>3</answer>
  </qa_pair>
<!-- More qa_pairs... -->
</evaluation>

Reference Files

📚 Documentation Library

Load these resources as needed during development:

Core MCP Documentation (Load First)

MCP Protocol: Start with sitemap at https://modelcontextprotocol.io/sitemap.xml, then fetch specific pages with .md suffix
📋 MCP Best Practices - 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 - Complete Python/FastMCP guide with:
- Server initialization patterns
- Pydantic model examples
- Tool registration with @mcp.tool
- Complete working examples
- Quality checklist
⚡ TypeScript Implementation Guide - 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 - 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

Files

scripts/connections.py

"""Lightweight connection handling for MCP servers."""

from abc import ABC, abstractmethod
from contextlib import AsyncExitStack
from typing import Any

from mcp import ClientSession, StdioServerParameters
from mcp.client.sse import sse_client
from mcp.client.stdio import stdio_client
from mcp.client.streamable_http import streamablehttp_client


class MCPConnection(ABC):
    """Base class for MCP server connections."""

    def __init__(self):
        self.session = None
        self._stack = None

    @abstractmethod
    def _create_context(self):
        """Create the connection context based on connection type."""

    async def __aenter__(self):
        """Initialize MCP server connection."""
        self._stack = AsyncExitStack()
        await self._stack.__aenter__()

        try:
            ctx = self._create_context()
            result = await self._stack.enter_async_context(ctx)

            if len(result) == 2:
                read, write = result
            elif len(result) == 3:
                read, write, _ = result
            else:
                raise ValueError(f"Unexpected context result: {result}")

            session_ctx = ClientSession(read, write)
            self.session = await self._stack.enter_async_context(session_ctx)
            await self.session.initialize()
            return self
        except BaseException:
            await self._stack.__aexit__(None, None, None)
            raise

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        """Clean up MCP server connection resources."""
        if self._stack:
            await self._stack.__aexit__(exc_type, exc_val, exc_tb)
        self.session = None
        self._stack = None

    async def list_tools(self) -> list[dict[str, Any]]:
        """Retrieve available tools from the MCP server."""
        response = await self.session.list_tools()
        return [
            {
                "name": tool.name,
                "description": tool.description,
                "input_schema": tool.inputSchema,
            }
            for tool in response.tools
        ]

    async def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> Any:
        """Call a tool on the MCP server with provided arguments."""
        result = await self.session.call_tool(tool_name, arguments=arguments)
        return result.content


class MCPConnectionStdio(MCPConnection):
    """MCP connection using standard input/output."""

    def __init__(self, command: str, args: list[str] = None, env: dict[str, str] = None):
        super().__init__()
        self.command = command
        self.args = args or []
        self.env = env

    def _create_context(self):
        return stdio_client(
            StdioServerParameters(command=self.command, args=self.args, env=self.env)
        )


class MCPConnectionSSE(MCPConnection):
    """MCP connection using Server-Sent Events."""

    def __init__(self, url: str, headers: dict[str, str] = None):
        super().__init__()
        self.url = url
        self.headers = headers or {}

    def _create_context(self):
        return sse_client(url=self.url, headers=self.headers)


class MCPConnectionHTTP(MCPConnection):
    """MCP connection using Streamable HTTP."""

    def __init__(self, url: str, headers: dict[str, str] = None):
        super().__init__()
        self.url = url
        self.headers = headers or {}

    def _create_context(self):
        return streamablehttp_client(url=self.url, headers=self.headers)


def create_connection(
    transport: str,
    command: str = None,
    args: list[str] = None,
    env: dict[str, str] = None,
    url: str = None,
    headers: dict[str, str] = None,
) -> MCPConnection:
    """Factory function to create the appropriate MCP connection.

    Args:
        transport: Connection type ("stdio", "sse", or "http")
        command: Command to run (stdio only)
        args: Command arguments (stdio only)
        env: Environment variables (stdio only)
        url: Server URL (sse and http only)
        headers: HTTP headers (sse and http only)

    Returns:
        MCPConnection instance
    """
    transport = transport.lower()

    if transport == "stdio":
        if not command:
            raise ValueError("Command is required for stdio transport")
        return MCPConnectionStdio(command=command, args=args, env=env)

    elif transport == "sse":
        if not url:
            raise ValueError("URL is required for sse transport")
        return MCPConnectionSSE(url=url, headers=headers)

    elif transport in ["http", "streamable_http", "streamable-http"]:
        if not url:
            raise ValueError("URL is required for http transport")
        return MCPConnectionHTTP(url=url, headers=headers)

    else:
        raise ValueError(f"Unsupported transport type: {transport}. Use 'stdio', 'sse', or 'http'")

Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Names Claude Code tools, Needs an MCP server, Needs network access, Needs Python, Needs Node.js

Security

What our scanner noticed· Low risk

Instructions that use the network
Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by

Anthropic

Publisher

JetBrains (Official)

Distributed by

JetBrains

Source

JetBrains/skills · mcp-builder · commit 3f53782

Licence

Apache License 2.0 (redistribution allowed)

ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Anthropic or JetBrains.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download mcp-builder.zip