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4 min readby Lorenzo Meola

Open source MCP servers that are really paid services

An MCP repo can be MIT licensed and still be a client for a paid service. How to tell what is open, what is hosted, and what leaves your machine.

"Open source MCP server" covers at least three different things. It can be a server you run entirely on your own machine. It can be an open client for a hosted service, with a free tier or a paid plan behind it. Or it can be a repo that holds setup files and docs pointing at a URL. All three turn up in search results under the same label, and the MIT license in the repo covers very different amounts of what you are about to connect.

The license covers what is in the repo. A hosted service has its own terms of service, its own pricing, and its own idea of what happens to your data. When a listing says "MIT" and stops there, it has answered the smallest part of the question.

Security checklists cover another half

There are good security checklists for MCP servers. Rafter's review checklist treats a server as a process on your machine and walks through the publisher, the install-time behavior, the tools it exposes, how credentials are handled, the update model, logging, and a kill switch. It also says that most servers in the current ecosystem fail at least three of those checks.

That is the right way to look at a process you run locally. This post covers the other half: what the license promises, and what runs somewhere else. The two questions need separate answers, and a server can pass one and fail the other.

The server is open, the data isn't

ByteAsk Embedded MCP is a clean example. The server code is open under MIT, and you can run it with uv in a few commands. It exposes three tools: search_docs, get_context, and request_document. It returns verbatim snippets from firmware and embedded reference documents with a section and page citation, or an honest "no confident match" instead of an invented register value.

It ships with a small sample backend, so it works right away, though only on a couple of illustrative records. The full document library and the retrieval engine sit behind a hosted endpoint, and those parts are proprietary. The listing says so directly: only the server code is open. If you use the hosted endpoint, your queries go there.

The client is open, the backend is a service

ContextStream is MIT licensed on the client side. It gives coding agents memory across sessions and semantic code search. The search backend is a separate cloud service with a free tier of 10,000 monthly credits and no card required. The details worth reading are in its data handling docs. Indexing sends eligible project files to the hosted search by default. Transcript saving and hook-based capture are on by default too, and both can be turned off in its data handling settings. It can also capture local Git metadata such as commit hashes, branch names, and aggregate diff stats.

For a public project, that is fine. For a private codebase, it is a decision to make before you connect, not after. The install is a curl | sh script or a hosted endpoint for clients that support OAuth.

Paying per call, done well

SocialCrawl MCP puts 575 endpoints across 65 platforms behind one server. You need an API key, and signup gives you 100 free credits with no card. Four of its ten tools work without a key. The part we like is how it handles money. A pricing tool quotes the credit cost of a call before you make it, and requests are validated locally, so a malformed one fails without spending anything. For a paid backend, those are the right instincts: tell people the price first, and don't charge for mistakes.

Side by side

ServerWhat is openWhat is hostedFree to try
ByteAsk Embedded MCPServer code, MITDocument library and retrieval engineYes, on a small sample set
ContextStreamMCP server and client, MITSearch backend10,000 monthly credits, no card
SocialCrawl MCPServer, MITThe data API100 credits, no card

The repo that is only config

The third kind is the one we skip. A repo we turned down this month was labeled an MCP server, but it held a setup script, configuration snippets for a handful of editors, and a README with plans starting at $99 a month. The MIT license covered the config and the documentation. The service itself ran elsewhere, under its own terms of service.

The product may be fine. A visitor just had no code to read or run, so there was nothing for a directory to describe beyond a URL. It did offer free tools through a no-account endpoint, which is a point in its favor. It wasn't enough to carry a listing on its own, since the free tools were part of the hosted service too.

Five questions before you connect

  1. Where does the code run? A local process you can read is a different thing from a URL you point your agent at.
  2. What leaves your machine? Look for indexing, transcript capture, and telemetry, and check whether each can be turned off.
  3. What does it cost, and is there a real free tier? Check whether a card is required, and whether the free tier lets you test the actual feature or only a demo.
  4. What can the key do? Prefer servers that issue scoped keys. One server we looked at offers read-only, build, and full scopes, which means you can give an agent that only needs to look a key that can only look.
  5. What does the license cover? Usually the repo. The hosted service has its own terms.

How we list them

We say the hosted part out loud. ContextStream carries a hosted tag, and the listings for ContextStream and ByteAsk each name which part is open and which isn't. We still list them, because open client code with a free tier is useful, and hiding where your data goes would not be. What decides it is whether the page can give you something to inspect and an honest description of the rest.

If you maintain a server like this, that is the shape of listing that works best. Put in the README what the repo contains, what runs on your side, what the free tier includes, and what the client sends by default. Then send it in.

#mcp#hosted#security#open-source
LM

Lorenzo Meola

I use agentic AI daily in my day job to build tools, and I've spent months learning how these systems actually work under the hood. I'm not an AI expert by title, just someone building this directory so other developers can find AI assets that are genuinely useful and verified to work, not just indexed.

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