.build/direct/example/mcp-server-orchestrator/SKILL.md
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.
npx skillsauth add organvm-iv-taxis/a-i--skills mcp-server-orchestratorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Manage MCP server infrastructure for AI-powered development workflows.
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ MCP Client │────▶│ MCP Server │────▶│ External APIs │
│ (Claude, etc.) │◀────│ (Tool Provider) │◀────│ (Services) │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│ │
└───── JSON-RPC ────────┘
Key concepts:
| 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 |
{
"mcpServers": {
"server-name": {
"command": "executable",
"args": ["arg1", "arg2"],
"env": {
"API_KEY": "value"
},
"disabled": false
}
}
}
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"
}
}
}
Verify server starts independently:
# Test Python server
python -m my_server
# Test Node server
npx -y @scope/package-name
Check logs:
~/Library/Logs/Claude/mcp*.logValidate JSON config:
python -c "import json; json.load(open('config.json'))"
Common fixes:
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()
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);
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", "args": ["-m", "my_tools"] }
}
}
Think of servers as modules in a synthesizer—patch them together based on workflow needs:
references/server-templates.md - Boilerplate for common server typesreferences/debugging-guide.md - Detailed troubleshooting proceduresdevelopment
Dry-run audit + targeted cleanup for shell command history. Currently wraps atuin (stats today, prune, dedup with dated preview artifacts); extensible to zsh/bash/mcfly backends. Always previews before applying — apply commands are echoed for the human to run, never auto-executed. Triggers on "/shell-history-hygiene", "audit atuin", "audit shell history", "clean shell history", "atuin prune", "atuin dedup", "shell history hygiene", "history cleanup". Replaces ad-hoc one-liners (e.g. `... | tee cmd > file.txt` which wrote two files, swallowed dedup output, and left a junk `cmd` file).
tools
Guided Cowork setup — install role-matched plugins, connect your tools, try a skill.
development
Manage AI agent session lifecycles with structured phases (FRAME, SHAPE, BUILD, PROVE), context preservation across sessions, handoff protocols, and session metadata tracking. Triggers on session management, agent lifecycle, or multi-session workflow requests.
tools
Parse a session transcript into a structured Session Governance Index — an annotated bibliography of every file modified and commit made, internal-energy accounting (tool uses, estimated tokens), shipped-vs-tasked atom tally, and classification of missing items as Gaps or Vacuums. Triggers on "visibility-schema-substrate-sweep", "session cascade audit", "session governance audit", or any request to summarize what a session actually produced versus what it was asked to produce.