Hey folks, Sarmad from the LastMile team here that built MCP-c and mcp-agent.
We're looking for thoughts and feedback so will be happy to take any questions here! And as the post shows, it's really easy to try this out -- would be grateful for you to try deploying an agent as an MCP server and get your impressions!
Thanks for sharing, this looks great! Do they have an MCP server? It should be easy to wrap around their JSON API but I couldn't see MCP support in the repo/docs.
So the core idea is the Deep Orchestrator is pretty unopinionated on what to use for searching, as long as it is exposed over MCP. I tried with a basic fetch server that's one of the reference MCP servers (with a single tool called `fetch`), and also tried with Brave.
I think the folks at Jina wrote some really good stuff on the actual search part: https://jina.ai/news/a-practical-guide-to-implementing-deeps... -- and how to do page/url ranking over the course of the flow. My recommendation would be to do all that in an MCP server itself. That keeps the "deep orchestrator" architecture fairly clean, and you can plug in increasingly sophisticated search techniques over time.
OP here -- I think the general principle I would recommend is using a big reasoning model for the planning phase. I think Claude Code and other agents do the same. The reason this is important is because the quality of the plan really affects the final result, and error rates will compound if the plan isn't good.
I think that's a fair point. How I envision this to realistically evolve is that MCP servers will expose workflows that handle common tasks. These workflows will be "agentic" because they'll involve LLMs interacting with tools and data, and it will be facilitated over MCP. For example, it would be great to have a "triage" workflow agent exposed by Linear, which in turn might use some MCP servers to make tool calls etc.
I don't know of a usecase where there are such deep recursive agent chains that it becomes unmanageable.
I almost think of mcp-agents as a modern form of scripting – we have agent workflows (e.g. generating a summary of new GitHub issues and posting on Slack), and exposing them as MCP servers has enabled us to use them in our favorite MCP clients.
The nice thing about representing agents as MCP servers is we can leverage distributed tracing via OTEL to log multi-agent chains. Within the agent application, mcp-agent tracing follows the LLM semantic conventions from OpenTelemetry (https://opentelemetry.io/docs/specs/semconv/gen-ai/). For any MCP server that the agent uses, we propagate the trace context along.
Authentication and authorization is something we are thinking about a lot at the moment, especially for agents that are MCP servers.
Our thoughts here are to handle auth the same way that the MCP spec outlines auth (https://modelcontextprotocol.io/specification/2025-03-26). The key thing is to send authorization requests back to the user in a structured way. For example, if Agent A invokes Agent B, which requires user approval for executing a tool call, that authorization request needs to be piped back to the client, and then propagated back to the agent.
This is technically possible to do with the MCP protocol as it exists today, but I think we will want to add that support in mcp-agent itself so it is easy to pause an agent workflow waiting for authentication/authorization.
One nice property of representing agents as MCP servers is that Agent discovery is the same as server discovery.
You should comment on the issue! I would love native support as well, but I also understand the desire to keep the framework lightweight. It should be fairly straightforward to add an extension package
Creator of mcp-agent here, OP and I are going to be keeping these projects in sync, and build agent apps on top of these patterns. MCP is pretty new and people are adding lots of servers for different software services. I think the next step is exposing agents (i.e. compound AI workflows) themselves as MCP servers.
In addition to simple abstractions for declaring agents, fastagents has a really nice CLI utility, which can be useful for interaction with MCPs outside of Cursor and Claude Desktop, where they are most popular atm.
This is something I have thought a little bit about. I think a registry would be useful and I know a few projects trying to set that up, but there may be multiple MCP servers for the same thing (already there are a few community implementations of an Atlassian server for e.g.).
It is conceivable that we make it completely dynamic where the agent first decides which set of servers it should need for its task/instruction. Another way of framing that is it should be possible to create agents themselves dynamically based on the objective.
I don’t have a good answer to this yet but if you want to help figure that out we can collaborate
It’s possible commenter is referring to my team. But yes this project was solely my fault :D, along with a few amazing contributors from the open source community who are credited in the readme
Ah, just saw your edited comment.
I am open to suggestions about this. These names are defined in the mcp_agent.config.yaml (e.g. https://github.com/lastmile-ai/mcp-agent/blob/main/examples/...). The logic that manages keeping track of them is mcp_server_registry.py.
I am open to alternative suggestions for this. I just wanted to make it easy to reference MCP servers by name and hide the logic of how they are initialized, what transport they use, what args are passed to them, into configs
We're looking for thoughts and feedback so will be happy to take any questions here! And as the post shows, it's really easy to try this out -- would be grateful for you to try deploying an agent as an MCP server and get your impressions!