The Best MCP Servers for Giving Agents Real Data

The Model Context Protocol is an open standard for connecting AI applications to external systems, and an MCP server is the side of that contract that owns a capability. It advertises its tools, receives calls, does the work and returns a result. If you want the protocol itself explained properly, we wrote a plain-English guide to MCP servers.

Judging servers is where most write-ups go wrong, because they count tools. Tool count is close to meaningless: a server with thirty vaguely described tools is harder for a model to use well than one with eight precisely described ones, since the model picks by reading descriptions at run time. Three things matter more.

First, whether the server owns data the agent genuinely cannot reach otherwise. Second, whether its authority is scoped, so a read-only task can be given a read-only key. Third, and most neglected, what comes back with the answer: a value alone, or a value with the address it came from and the date it was read.

That third question is what separates the categories below. A server reading your own filesystem does not need provenance, because you already know where the file is. A server answering questions about the outside world does, because the model has no other way to tell a fact from a plausible sentence.

Almost every server worth connecting falls into one of five groups, and the groups differ in a way that matters: how much the agent should trust the result without further checking.

The table sets them side by side. Read the last column first — it is the one that decides whether an agent can act on the result or merely summarise it.

This is the category most people meet first, and it is the one where MCP is least controversial. The reference servers published alongside the protocol cover the obvious primitives — a filesystem server for reading and writing local files, a git server for repository operations, a fetch server for pulling a URL into the context window, and small helpers for memory and time.

GitHub publishes its own official MCP server, which is the pattern to expect from any vendor with a good API: repositories, issues, pull requests and reviews arrive as structured objects rather than as scraped HTML, and the agent's authority is whatever the token grants. Databases follow the same shape, with a Postgres reference server and an increasing number of vendor-published equivalents.

What unites them is that the trust question is already settled. The agent is reading systems you control, and if the data in them is wrong, that is a problem the MCP server did not create and cannot fix. Provenance is unnecessary because the path, the repository or the table already is the provenance.

The limit is equally clear: none of these servers know anything about the world outside your walls. An agent with all three connected still cannot tell you that a supplier was sanctioned last Friday.

The second family reaches outside. Brave Search, Exa and Firecrawl all publish MCP servers, and between them they cover the two obvious needs: find pages that match a query, and turn a page into something a model can read. For open-ended research this is the right tool, and it is often the fastest route from a question to a usable answer.

What they return is results and content as of the moment of the call. That is genuinely live, and it is not the same as maintained. The model gets a URL, which is a real citation and a real improvement on nothing, but it does not get a record of what that page said last week, whether the value it just read has moved, or how many independent origins agree with it.

The practical consequence appears the second time you ask. Search-and-fetch answers a question well; it does not answer “what changed since the last time I asked” at all, because there is no stored prior state to compare against. Every run starts from zero, which also means every run can disagree with the last one without anything being wrong.

The fifth category is the one we build in, so read this section knowing whose it is. The premise is that some outside-world questions are asked repeatedly and acted upon, and those deserve to be maintained sets rather than fresh searches: public procurement notices, end-of-life dates, executive moves, price changes, sanctions actions, auction lots, funding filings.

Maintained means each set re-runs on its own cadence and keeps its prior state, so “what is new since Tuesday” is a query rather than a diff against a copy you hopefully kept. Receipted means every value carries the source URL it was read from, the date it was observed, and an explicit confidence marker — CONFIRMED where two or more independent origins agree, SIGNAL where a single origin is all there is.

The scraper.io MCP server is a remote Streamable HTTP server with ten typed tools: list_monitors, get_monitor, create_monitor, update_monitor, pause_monitor, resume_monitor, list_alerts, get_evaluation, list_channels and check_now. Six of those manage monitored sets, three read what happened, and check_now forces a run. Read-only agents can be issued read-only keys, creating or changing a monitor requires an explicit write scope, and every call is isolated to a single workspace.

The honest limit is coverage. A search server can attempt any question; a feed server can only serve the sets that exist, and building a new one is work rather than a query. The trade is depth for breadth, and it is only the right trade when the same question is going to be asked again.

Start from the gap rather than the catalogue. Write down the sentence the agent cannot currently complete, then pick the category that owns the missing half of it. Most useful agent setups end up with three or four servers from different categories rather than many from one.

A short sequence resolves most cases. If the missing data is yours, connect the system that holds it and stop. If the question is open-ended and asked once, connect a search or retrieval server. If the question is narrow, recurring, and its answer will trigger an action, put a maintained feed behind it so the model receives a value it can cite.

Two cautions apply regardless of category. Give every server the smallest scope that lets the task succeed, because a tool the model can call is a tool a badly worded page can talk it into calling. And read the tool descriptions before you connect a server: they are the interface the model actually sees, and a server whose descriptions are vague will be used badly no matter how good its data is.

If you want to see what a receipted set looks like before wiring anything up, the live feeds are the same data the tools above serve, with the source link visible on every row.