mcp-server-orchestrator

name	mcp-server-orchestrator
description	Configure, deploy, and troubleshoot Model Context Protocol (MCP) servers for AI agent workflows. Use when setting up MCP servers, debugging connection issues, managing multi-server configurations, integrating with Claude Desktop/Code/Cowork, or designing custom tool servers. Triggers on MCP configuration, tool server development, Claude integration issues, or agent infrastructure setup. Use when this capability is needed.
metadata	{"author":"a-organvm"}

MCP Server Orchestrator

Manage MCP server infrastructure for AI-powered development workflows.

MCP Architecture Overview

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   MCP Client    │────▶│   MCP Server     │────▶│  External APIs  │
│ (Claude, etc.)  │◀────│  (Tool Provider) │◀────│  (Services)     │
└─────────────────┘     └──────────────────┘     └─────────────────┘
         │                       │
         └───── JSON-RPC ────────┘

Key concepts:

• Server: Provides tools, resources, and prompts via MCP protocol
• Client: Consumes server capabilities (Claude Desktop, Claude Code, etc.)
• Transport: Communication layer (stdio, SSE, WebSocket)

Configuration Locations

Client	Config File	Platform
Claude Desktop	claude_desktop_config.json	macOS: ~/Library/Application Support/Claude/
		Windows: %APPDATA%\Claude\
Claude Code	settings.json or MCP config	Project-level or user settings
Cline	cline_mcp_settings.json	VS Code extension settings

Server Configuration Schema

{
  "mcpServers": {
    "server-name": {
      "command": "executable",
      "args": ["arg1", "arg2"],
      "env": {
        "API_KEY": "value"
      },
      "disabled": false
    }
  }
}

Common Server Types

Python Server (uvx):

{
  "my-python-server": {
    "command": "uvx",
    "args": ["--from", "package-name", "server-command"]
  }
}

Node Server (npx):

{
  "my-node-server": {
    "command": "npx",
    "args": ["-y", "@scope/package-name"]
  }
}

Local Development Server:

{
  "dev-server": {
    "command": "python",
    "args": ["-m", "my_server"],
    "env": {
      "DEBUG": "true"
    }
  }
}

Troubleshooting Workflow

Connection Issues

1. Verify server starts independently:

   # Test Python server
   python -m my_server
   
   # Test Node server
   npx -y @scope/package-name
   

2. Check logs:

   • Claude Desktop: ~/Library/Logs/Claude/mcp*.log
   • Look for JSON-RPC errors, connection timeouts

3. Validate JSON config:

   python -c "import json; json.load(open('config.json'))"
   

4. Common fixes:

   • Use absolute paths for commands
   • Ensure dependencies installed in correct environment
   • Check API keys/env vars are set
   • Restart client after config changes

Authentication Issues

1. OAuth flows: Ensure redirect URIs configured correctly
2. API keys: Verify env vars accessible to server process
3. Token refresh: Check token storage location and permissions

Building Custom Servers

Python Server (FastMCP)

from fastmcp import FastMCP

mcp = FastMCP("my-server")

@mcp.tool()
def my_tool(param: str) -> str:
    """Tool description for the AI."""
    return f"Result: {param}"

@mcp.resource("resource://my-data")
def get_data() -> str:
    """Provide data as a resource."""
    return "Resource content"

if __name__ == "__main__":
    mcp.run()

Node Server (MCP SDK)

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({ name: "my-server", version: "1.0.0" }, {
  capabilities: { tools: {} }
});

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [{
    name: "my_tool",
    description: "Tool description",
    inputSchema: { type: "object", properties: { param: { type: "string" } } }
  }]
}));

const transport = new StdioServerTransport();
await server.connect(transport);

Multi-Server Orchestration

Modular Architecture

Organize servers by domain:

{
  "mcpServers": {
    "filesystem": { "command": "...", "args": ["--allowed-dirs", "/projects"] },
    "database": { "command": "...", "env": { "DB_URL": "..." } },
    "api-integrations": { "command": "...", "env": { "API_KEYS": "..." } },
    "custom-tools": { "command": "python",    
  

Server Selection Strategy

Think of servers as modules in a synthesizer—patch them together based on workflow needs:

• Development workflow: filesystem + git + code-analysis servers
• Research workflow: web-search + document + note-taking servers
• Data workflow: database + visualization + export servers

Performance Optimization

• Lazy loading: Only enable servers needed for current task
• Caching: Implement response caching for expensive operations
• Timeout tuning: Adjust timeouts for slow external APIs
• Connection pooling: Reuse connections in database servers

References

• references/server-templates.md - Boilerplate for common server types
• references/debugging-guide.md - Detailed troubleshooting procedures

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