Connect LLMs to MCP servers through composable patterns like router, evaluator-optimizer, and orchestrator flows without hand-managing server lifecycles.
Scanned 6/8/2026
Install via CLI
openskills install agentskillexchange/skills---
name: "Compose router, evaluator, and handoff workflows over MCP servers with mcp-agent"
slug: "compose-router-evaluator-and-handoff-workflows-over-mcp-servers-with-mcp-agent"
description: "Connect LLMs to MCP servers through composable patterns like router, evaluator-optimizer, and orchestrator flows without hand-managing server lifecycles."
github_stars: 8276
verification: "security_reviewed"
source: "https://github.com/lastmile-ai/mcp-agent"
author: "LastMile AI"
publisher_type: "open_source_project"
category: "Templates & Workflows"
framework: "MCP"
tool_ecosystem:
github_repo: "lastmile-ai/mcp-agent"
github_stars: 8276
npm_package: "mcp-agent"
npm_weekly_downloads: 29247
---
# Compose router, evaluator, and handoff workflows over MCP servers with mcp-agent
Connect LLMs to MCP servers through composable patterns like router, evaluator-optimizer, and orchestrator flows without hand-managing server lifecycles.
## Prerequisites
Python runtime, MCP servers, LLM API key, optional Temporal for durable workflows
## Installation
Use the upstream install or setup path that matches your environment:
- uv init
- uv add "mcp-agent[openai]"
- uv run main.py
- uv add "mcp-agent"
Requirements and caveats from upstream:
- python
- We recommend using [uv](https://docs.astral.sh/uv/) to manage your Python projects (uv init).
Basic usage or getting-started notes:
- <a id="minimal-example"></a>
- **Minimal example**
- async with app.run():
- Source: https://github.com/lastmile-ai/mcp-agent
- Extracted from upstream docs: https://raw.githubusercontent.com/lastmile-ai/mcp-agent/HEAD/README.md
## Documentation
- https://docs.mcp-agent.com/
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/compose-router-evaluator-and-handoff-workflows-over-mcp-servers-with-mcp-agent/)
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