servers VS mcp-agent

Most agents don't speak raw HTTP — they speak MCP. So the real distribution surface is a tiny MCP server that exposes each endpoint as a tool and does the paying under the hood. The agent calls web_search(query); the server hits the paid endpoint, handles the 402 with its operator's wallet, returns JSON. One line to install:

The Model Context Protocol (MCP) is an open standard for connecting AI hosts (Claude, ChatGPT, Cursor, VS Code, and others) to external context and actions through a structured protocol instead of ad-hoc plugins.

Model Context Protocol specification

That changed when I built mpesa-mcp — a Model Context Protocol server that wraps the Daraja API and makes it directly available to Claude, GPT-4, and Gemini.

If you haven't run into it yet, MCP (Model Context Protocol) is an open standard for exposing tools to AI agents. two-go ships an MCP server that runs over stdio with no dependencies, no URL, no account, and no API key. It's all local.

MCP server surface. The CLI is already agent-friendly, so I'm double minded about whether a Model Context Protocol server would make integration smoother. I'm personally not a fan of MCP and prefer skills and CLIs.

As noted in the Model Context Protocol (MCP) Introduction, this protocol allows Claude to interact with systems like GitHub and local databases through a standardized interface.

Keep the project's living context: current task state, decisions, conventions, known footguns as plain Markdown committed to the repo, and expose it to the agent over MCP.

The MCP ecosystem doubles every few months. Bookmark the official MCP servers repository for the canonical list.

Hello HN!

Earlier this year, we shared mcp-agent(https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework for building agents with MCP. Since then we have tried to push the protocol to the limits, including hosting agents as long-running tools on MCP [3], and seen other creative approaches surface (mcp-ui, chatgpt apps sdk).

Today, we are launching mcp-c – a cloud platform designed for hosting any kind of MCP server, including agents, ChatGPT apps, etc. We are in open beta and free to use, and would love your feedback.

Here are some key choices we made:

Haha, I didn't have control on the blog website, just the content. The readme and code is the ultimate source of truth (and easier to read):https://github.com/lastmile-ai/mcp-agent/blob/main/src/mcp_a...

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.

lastmile-ai/mcp-agent

So I built a universal research agent on top of mcp-agent a framework that lets you coordinate tools, APIs, memory, and LLM calls in one streamlined workflow.

That’s exactly what MCP Agent is built for.

This is exactly what we're working on at the moment! (If you're curious about following along progress, check out feature/distributed_tracing branch -- https://github.com/lastmile-ai/mcp-agent/blob/feature/distri...)

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.

MCP-agent Framework: https://github.com/lastmile-ai/mcp-agent

mcp-agent — a python based library to help you build AI agents that supports MCPs.