The Model Context Protocol (MCP) turned every AI agent into something you can plug real tools into. The question is which servers are actually worth connecting. Suda is one of them, and this guide sorts the field by what each type does so you can pick well instead of connecting everything and hoping.

How to judge an MCP server

Before the list, the test. A server is worth connecting if it clears three bars:

It returns something the agent cannot get on its own. Real, private, or current data, not general knowledge.
It returns it cleanly. Structured results the agent can use, not a wall of raw text.
It respects access. The agent sees only what the person behind it is allowed to see.

Fail the third and you have a data leak with a nice interface. Keep all three in mind as you connect.

The categories worth your time

1. Context servers

The highest-leverage category. A context server hands the agent connected, current context across all your tools at once, with stale facts retired and conflicts resolved before the agent asks. Suda is a context server: it ingests from more than 700 sources, builds a context graph, and serves the result over MCP. This is the one to start with, because it removes the cross-tool stitching that otherwise lands in the agent. See MCP context servers for how this differs from the rest.

2. Source-specific servers

Servers that wrap one app: a docs tool, a ticketing system, a single database. Useful when an agent needs deep, tool-specific actions, like creating or updating a record in that one system. The limit is fragmentation: connect ten of these and the agent has to know which to call and how to merge the results. Good for actions, weak as a way to give an agent broad context.

3. Action and automation servers

Servers that let the agent do things, not just read: send a message, open a ticket, trigger a job. Worth connecting once your agent is trusted to act, but scope them tightly. An agent that can write is an agent that can write the wrong thing.

4. Web and search servers

Servers that give the agent live web access or search. Good for public, current information the agent has no other way to reach. They do nothing for your private context, so treat them as a complement, not a substitute for a context server.

A sensible order to connect them

For a product or ops team, connect in this order:

A context server first. It covers the broadest need, your own company context, in one connection. Run npx suda connect and link your first source.
Source-specific servers next, only for tools where the agent needs deep actions the context layer does not cover.
Action servers, scoped to what you trust the agent to do.
Web or search servers last, for public information.

npx suda connect

Why start with context

Most teams reach for single-tool servers first because they are obvious, then drown in the merge problem. Starting with a context server flips that. The agent gets one reconciled view of your company, and you add narrow servers only where you need actions. You also send the model far less text, because the context layer passes only what the question needs. For Suda that is about 85% less per answer, which lowers token cost.

Common questions

How many MCP servers should one agent have? Fewer than you think. Every server you add is another thing the agent has to choose between. Cover the broad need with a context server, then add narrow servers only where you need specific actions.

Do more servers mean better answers? No. More connections mean more raw data to sort, not more signal. A context server that reconciles first beats five that each hand over a fragment.

What about permissioning across many servers? It gets harder with every server, because each has its own access model. A context server that carries permissions through is one place to get this right instead of many places to get it wrong.

To connect the specific tools most teams run, read connect Notion, Slack, and Linear to your AI.