Representing Agents as MCP Servers
Hey HN! A few months ago we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework that implements every agent pattern from Anthropic’s Building Effective Agents blog [3] and handles MCP server/client management seamlessly. Our core bet is that connecting LLMs to tools, resources, and external systems will soon be MCP-native by default. Today we're launching a significant update: Agents as MCP servers. Currently "agentic" behavior exists only on the MCP client side – clients like Claude or Cursor use MCP servers to solve tasks.…
In plain words
mcp-agent is a lightweight framework that enables AI agents to function as Model Context Protocol (MCP) servers. This allows any MCP client to invoke and orchestrate agents alongside other tools and resources using a unified protocol. The framework implements standard agent patterns and handles MCP server and client management. It is designed for developers building multi-agent systems and applications that need to integrate agentic behavior with existing MCP-compatible tools.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! A few months ago we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework that implements every agent pattern from Anthropic’s Building Effective Agents blog [3] and handles MCP server/client management seamlessly. Our core bet is that connecting LLMs to tools, resources, and external systems will soon be MCP-native by default. Today we're launching a significant update: Agents as MCP servers. Currently "agentic" behavior exists only on the MCP client side – clients like Claude or Cursor use MCP servers to solve tasks. With this update, Agents can be MCP servers themselves, so that any MCP client can invoke, coordinate and orchestrate agents the same way it does with any other MCP server. This paradigm shift enables: 1. Agent Composition: Build complex multi-agent systems over the same base protocol (MCP). 2. Platform Independence: Use your agents from any MCP-compatible client 3. Scalability: Run agent workflows on dedicated infrastructure, not just within client environments 4. Customization: Develop your own agent workflows and reuse them across any MCP client. How an agent server is implemented: We’ve implemented this in mcp-agent with Workflows. Each workflow is an agent application that can interact with other MCP servers (e.g. summarizing GitHub issues → Slack message). mcp-agent exposes workflows as MCP tools on an MCP Agent Server [5]: - workflows/list – list available workflows - workflows/{WorkflowName}/run – Execute the workflow (async) - workflows/{WorkflowName}/get_status – Check workflow status - workflows/{WorkflowName}/resume – Resume paused workflow (e.g. with human input) - workflows/{WorkflowName}/cancel – Terminate workflow We’ve also implemented Temporal for durable execution [6], so agent workflows can be paused, resumed and retried in production settings. This demo [7] shows Claude invoking an MCP agent server, running workflows when appropriate, and polling for status. It basically shows agentic behavior on both the MCP client and MCP server side. We're excited about the potential this unlocks—especially as more applications become MCP-compatible clients. We'd love your feedback and ideas! [1] - https://news.ycombinator.com/item?id=42867050 [2] - https://github.com/lastmile-ai/mcp-agent [3] - https://www.anthropic.com/research/building-effective-agents [4] - https://github.com/github/github-mcp-server [5] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/... [6] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/... [7] - https://youtu.be/pLe2GAjEoYs [DEMO]
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