mcp-server-orchestrator

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.

Best use case

mcp-server-orchestrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using mcp-server-orchestrator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/mcp-server-orchestrator/SKILL.md --create-dirs "https://raw.githubusercontent.com/organvm-iv-taxis/a-i--skills/main/distributions/claude/skills/mcp-server-orchestrator/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/mcp-server-orchestrator/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How mcp-server-orchestrator Compares

Feature / Agentmcp-server-orchestratorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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

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

### Common Server Types

**Python Server (uvx)**:
```json
{
  "my-python-server": {
    "command": "uvx",
    "args": ["--from", "package-name", "server-command"]
  }
}
```

**Node Server (npx)**:
```json
{
  "my-node-server": {
    "command": "npx",
    "args": ["-y", "@scope/package-name"]
  }
}
```

**Local Development Server**:
```json
{
  "dev-server": {
    "command": "python",
    "args": ["-m", "my_server"],
    "env": {
      "DEBUG": "true"
    }
  }
}
```

## Troubleshooting Workflow

### Connection Issues

1. **Verify server starts independently**:
   ```bash
   # 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**:
   ```bash
   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)

```python
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)

```typescript
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:

```json
{
  "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 types
- `references/debugging-guide.md` - Detailed troubleshooting procedures

Related Skills

pentaphase-orchestrator

5
from organvm-iv-taxis/a-i--skills

Threads the full five-phase structural-overhaul protocol — landscape discovery, taxonomy design, environment configuration, systemic ingestion, governance evolution — for any substrate the user names. Use when the user requests a structural overhaul, system redesign, or end-to-end restructuring of a documentation system, asset registry, code monorepo, knowledge base, or operational workflow; or when they explicitly invoke the pentaphase methodology. Coordinates handoffs between phase-skills and seats validation gates between phases.

project-alchemy-orchestrator

5
from organvm-iv-taxis/a-i--skills

A strategic guide for managing complex creative portfolios as a system of "organs" (Art, Commerce, Tools), diagnosing lifecycle stages (Nigredo, Albedo, Rubedo), and balancing the "Four Fields" of creative labor.

feature-workflow-orchestrator

5
from organvm-iv-taxis/a-i--skills

End-to-end feature development orchestration from planning through deployment with quality gates

agent-swarm-orchestrator

5
from organvm-iv-taxis/a-i--skills

Designs multi-agent systems with coordinated agent swarms, task distribution, inter-agent communication, and emergent collective behavior.

taxonomy-modeling-design

5
from organvm-iv-taxis/a-i--skills

Phase 2 of the pentaphase structural-overhaul protocol. Classifies entities, standardizes attributes, establishes relationships, and designs the access framework. Use when the user invokes phase 2 of an overhaul, asks to "design the taxonomy" or "model the structure", or has completed a landscape audit and is ready to redesign. Consumes phase-1-landscape-report.md; produces phase-2-taxonomy-model.md.

systemic-ingestion-normalization

5
from organvm-iv-taxis/a-i--skills

Phase 4 of the pentaphase structural-overhaul protocol. Purges redundancies, enriches and aligns legacy entities to the new schema, executes phased ingestion into the new environment, and audits integrity. Use when the user invokes phase 4 of an overhaul, asks to "migrate the data" or "ingest into the new system", or has a configured environment ready to accept legacy entities. Consumes phase-3-environment-spec.md; produces phase-4-ingestion-report.md.

system-environment-configuration

5
from organvm-iv-taxis/a-i--skills

Phase 3 of the pentaphase structural-overhaul protocol. Translates the taxonomy model into objective technical criteria, evaluates candidate mechanisms or frameworks, instantiates the chosen architecture, and programs validation rules. Use when the user invokes phase 3 of an overhaul, asks to "select a system" or "configure the environment", or has a taxonomy model and is ready to choose technology. Consumes phase-2-taxonomy-model.md; produces phase-3-environment-spec.md.

landscape-discovery-audit

5
from organvm-iv-taxis/a-i--skills

Phase 1 of the pentaphase structural-overhaul protocol. Inventories assets, maps current flow, identifies friction, and defines value metrics for any substrate. Use when the user invokes phase 1 of an overhaul, requests a baseline audit, asks to "discover the landscape" of a system, or wants to understand current state before redesigning. Produces phase-1-landscape-report.md.

governance-evolution-protocol

5
from organvm-iv-taxis/a-i--skills

Phase 5 of the pentaphase structural-overhaul protocol. Codifies operational protocols, onboards the ecosystem of participants, programs behavior monitoring, and establishes an iteration cadence so the substrate evolves rather than calcifies. Use when the user invokes phase 5 of an overhaul, asks to "establish governance" or "lock in the protocols", or has completed ingestion and is ready to declare the substrate operational. Consumes phase-4-ingestion-report.md; produces phase-5-governance-charter.md, which closes the protocol.

dimension-surfacing

5
from organvm-iv-taxis/a-i--skills

Surfaces the parallel domain dimensions implicit in a dense or minimal prompt. Use when a user prompt is small on the surface but plainly implies multiple independent domains needing different expertise; when explicitly invoked by the coliseum-orchestrator skill as Phase 1; or when the user asks "what dimensions does this prompt encode" or "what axes does this break into." Produces a named dimension set where each dimension is independently executable and not a paraphrase of another.

coliseum-dispatch

5
from organvm-iv-taxis/a-i--skills

Dispatches a composed set of assignment envelopes to domain-expert subagents in parallel, in a single message with multiple Agent tool calls. Enforces the no-pingpong gate via the pingpong-detector agent before any dispatch fires. Use when invoked by the coliseum-orchestrator as Phase 3; when envelopes are already composed and the next step is parallel execution; or when the user asks to "fan out" or "dispatch in parallel." Produces a dispatch log capturing what was sent, when, and where returns land.

assignment-composition

5
from organvm-iv-taxis/a-i--skills

Wraps each surfaced dimension as a self-contained 9-section autonomous-work-assignment envelope — scope, context, success criteria, allowed tools, return format, handoff — all the recipient subagent needs to execute without coming back. Use when invoked by coliseum-orchestrator as Phase 2; when dimensions are named and the next step is to make each independently dispatchable; or when the user asks "compose this as an assignment." The no-pingpong gate validates each envelope before dispatch.