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Creating custom servers that expose tools, resources, and prompts through the Model Context Protocol

Core Idea: Building MCP servers involves creating standardized endpoints that package tools, data resources, and prompt templates for LLMs to consume, enabling interoperability and reuse across different AI applications.

Key Elements

Development Approaches

• SDK-Based: Using official Anthropic SDKs for Python or JavaScript
• Framework-Based: Building on top of existing web frameworks with MCP libraries
• LLM-Assisted: Using AI assistants like Claude to generate server code based on MCP documentation

Architecture Considerations

• Server Hosting: Local vs. cloud deployment options
• Stateful vs. Stateless: Managing connection state between requests
• Authentication: Implementing secure access control when needed
• Error Handling: Graceful failure and informative error messages

Implementation Steps

1. Choose SDK:

   • Python SDK with UVX for Python-based servers
   • JavaScript SDK with Node.js for JavaScript-based servers

2. Define Tools:

   • Create function signatures with proper parameter types
   • Write clear descriptions for LLM understanding
   • Implement actual functionality in handler functions

3. Add Resources (optional):

   • Define document schemas
   • Implement resource fetching logic
   • Configure caching behavior

4. Create Prompts (optional):

   • Design template structures
   • Define parameter schemas
   • Implement rendering logic

5. Test Server:

   • Validate with local clients
   • Test with sample requests
   • Verify error handling

Code Example (Python)

from mcp import MCPServer, define_tool, Resource

# Create server instance
server = MCPServer("My Custom Tools")

# Define a tool with parameters
@server.tool(
    name="database.query",
    description="Run a query against the database",
    parameters={
        "query": {
            "type": "string",
            "description": "SQL query to execute"
        },
        "database": {
            "type": "string",
            "description": "Database name",
            "default": "main"
        }
    }
)
async def execute_query(query: str, database: str = "main"):
    # Implementation logic here
    result = db_connection.execute(query, db=database)
    return {"rows": result.rows, "count": len(result.rows)}

# Define a resource
@server.resource(
    name="documentation",
    description="API documentation"
)
async def get_documentation():
    with open("docs.md", "r") as f:
        return Resource(content=f.read(), mime_type="text/markdown")

# Start the server
if __name__ == "__main__":
    server.start(host="localhost", port=8080)

Common Challenges

• Input Validation: Ensuring proper parameter types and validation
• Error Handling: Graceful failure and informative error messages
• Documentation: Clear descriptions for LLM understanding
• Performance: Efficient handling of requests, especially for resource-intensive operations

Best Practices

Tool Design

• Use descriptive names with namespaces (e.g., database.query)
• Provide clear, concise descriptions of tool functionality
• Use parameter schemas with type hints and descriptions
• Implement proper error handling and validation

Security Considerations

• Validate and sanitize inputs to prevent injection attacks
• Implement proper authentication for sensitive operations
• Limit access to system resources
• Follow principle of least privilege

Documentation

• Include example usage in tool descriptions
• Document expected inputs and outputs clearly
• Provide example code for client integration
• Create README files for server configuration

LLM-Assisted Development

Using Claude or other AI assistants to build MCP servers:

1. Share MCP documentation with the LLM
2. Describe the desired server functionality
3. Request complete implementation code
4. Review and modify the generated code
5. Test with sample requests

Connections

• Protocol Related: Model Context Protocol, MCP Architecture, MCP Servers
• Implementation: MCP Python SDK, MCP JavaScript SDK, LLM-Assisted Development
• Usage: MCP Clients, Claude Desktop Integration, n8n Integration
• Applications: Custom Tool Development, Database Tool Example, Web Scraping with MCP

References

1. MCP Server Development Guide: modelcontextprotocol.io/server-dev
2. Python MCP SDK Documentation: github.com/anthropics/mcp-python
3. JavaScript MCP SDK Documentation: github.com/anthropics/mcp-js
4. Example Server Implementations: github.com/anthropics/mcp-examples

#MCPServer #BuildingMCP #ToolDevelopment #AITools #PythonSDK #JavaScriptSDK #ServerDevelopment

Connections:

Sources:

• From: Cole Medin - Claude MCP ha cambiado la IA para siempre esto es lo que NECESITAS saber 1