mcp-server-orchestrator
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- DevOps
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Manual integration
- External API key
- Required · Vendor-specific
- Operating systems
- macOS · Windows
- Runtime requirements
- Node.js · Python
- Permissions
-
- Read-only
- Shell exec
- Env read
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: mcp-server-orchestrator
description: Configure, deploy, and troubleshoot Model Context Protocol (MCP) servers for AI agent workflows.…
category: devops
runtime: Node.js / Python
---
# mcp-server-orchestrator output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “MCP Architecture Overview / Configuration Locations / Server Configuration Schema” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “MCP Architecture Overview / Configuration Locations / Server Configuration Schema” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, run shell commands, read environment variables, write/modify files; may access external network resources; requires Vendor-specific API keys.
## Running Rules
- read files, run shell commands, read environment variables, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, run shell commands, read environment variables, write/modify files.
Start with a small task and check whether the result follows “MCP Architecture Overview / Configuration Locations / Server Configuration Schema”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: mcp-server-orchestrator
description: Configure, deploy, and troubleshoot Model Context Protocol (MCP) servers for AI agent workflows.…
category: devops
source: tomevault-io/skills-registry
---
# mcp-server-orchestrator
## When to use
- Configure, deploy, and troubleshoot Model Context Protocol (MCP) servers for AI agent workflows. Use when setting up M…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “MCP Architecture Overview / Configuration Locations / Server Configuration Schema” and keep inference separate from source facts.
- read files, run shell commands, read environment variables, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "mcp-server-orchestrator" {
input -> user goal + target files + boundaries + acceptance criteria
context -> MCP Architecture Overview / Configuration Locations / Server Configuration Schema
rules -> SKILL.md triggers / order / output contract
runtime -> Node.js / Python | read files, run shell commands, read environment variables, write/modify files | may access external network resources
guardrails -> requires Vendor-specific API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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
Verify server starts independently:
# Test Python server python -m my_server # Test Node server npx -y @scope/package-nameCheck logs:
- Claude Desktop:
~/Library/Logs/Claude/mcp*.log - Look for JSON-RPC errors, connection timeouts
- Claude Desktop:
Validate JSON config:
python -c "import json; json.load(open('config.json'))"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
- OAuth flows: Ensure redirect URIs configured correctly
- API keys: Verify env vars accessible to server process
- 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", "args": ["-m", "my_tools"] }
}
}
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 typesreferences/debugging-guide.md- Detailed troubleshooting procedures
Source: a-organvm/a-i--skills — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review