An MCP server gives an agent access to a particular set of tools and information. That might be your GitHub issues, your team's Notion pages, a browser, or a web search service.
The useful question is what you want the agent to accomplish. A coding agent needs different connections from an agent preparing a customer brief. Installing ten servers before defining the task usually leaves you with more setup and a less obvious place to start.
These are ten servers we would consider for common agent workflows. We selected them for a clear purpose and documentation maintained by the provider or project. Sources were checked on September 8, 2026. This is a task-based shortlist, not a benchmark ranking or a claim that we tested every integration in Dexto.
The best MCP servers by task
| Server | Where we would start | A useful first task |
|---|---|---|
| GitHub | Repository and pull-request context | Summarize the changes and unresolved questions in a PR |
| Context7 | Library documentation | Find the documented API for the version you use |
| Linear | Issue and project tracking | Turn open issues into a release-status brief |
| Notion | Team knowledge | Find the decisions behind a project |
| Sentry | Production debugging | Investigate a recurring error |
| Playwright MCP | Browser interaction | Walk through a local feature and report what happened |
| Chrome DevTools MCP | Browser diagnostics | Explain a slow page using a performance trace |
| Exa | Web research | Find original sources for a technical comparison |
| Firecrawl | Reading and extracting web content | Turn relevant pages into a structured brief |
| Stripe | Billing operations | Investigate a test-mode customer and subscription |
A small starting set is often enough. For coding, we would begin with GitHub and Context7, then add browser access when the task requires it. For research, begin with search and a place to read or organize the results.
GitHub
Our pick for work that starts with a repository, issue, or pull request.
The official GitHub MCP server exposes repository, issue, pull-request, and workflow capabilities. GitHub offers a hosted remote server as well as a local option, and documents ways to limit the available toolsets.
A useful first request is: “Read this PR and its linked issue. Explain what changed, identify unanswered review questions, and link to the relevant discussion.” That produces something you can check before asking the agent to make changes.
Choose the permissions around the job. Reviewing a PR should not require granting every repository operation. Also distinguish reading code on GitHub from running it: this connection does not, by itself, give the agent a working checkout and test environment.
Context7
Our pick for bringing library documentation into coding tasks.
Context7 provides current, version-specific documentation and examples to AI coding tools. It is useful when an agent knows the general shape of a library but needs the actual API your project uses.
Try: “Check the installed version of this library, find its documentation, and explain how this configuration should be written.” Give the agent the package version rather than assuming the newest documentation applies.
Documentation is one input to a coding decision. The agent should still inspect local types, existing usage, and tests. If the library or version is unavailable, it should say so and use the project's original documentation instead of silently substituting another version.
Linear
Our pick for turning issue-tracker context into an actionable work brief.
Linear's MCP server lets compatible clients work with Linear data, including issues and projects. It is a natural fit when the work is already organized there.
Start with: “Summarize the open issues for this release. Group blockers separately and link every statement to an issue.” Ask the agent to distinguish information explicitly recorded in Linear from its own interpretation.
A tracker is only as complete as the updates people put in it. An issue that has not changed for a week is not necessarily blocked. For write operations, have the agent show proposed changes before it updates status, ownership, or descriptions.
Notion
Our pick for agents that need the team's written context.
Notion MCP connects supported AI tools to a Notion workspace. It helps an agent work with the pages and knowledge your team already maintains rather than asking you to paste a new briefing into every conversation.
Try: “Find the project brief and decision notes for this launch. Summarize what is agreed, what is unresolved, and which pages support each point.”
Be specific about the project and time period. Teams often have several pages describing different stages of the same decision. The agent should surface conflicting documents and keep their links, not compress them into a falsely confident answer. Confirm that the connected account can access the pages the task needs.
Sentry
Our pick for giving a debugging agent production-error context.
Sentry MCP exposes Sentry information to AI assistants for investigating application problems. It is useful alongside repository access: an error report describes what happened in production, while the code helps explain why.
A good first task is: “Investigate this issue. Summarize the error, identify relevant evidence, and propose what to inspect in the repository next.”
Keep the distinction between a hypothesis and a verified fix. A plausible explanation based on a stack trace still needs to be checked against the affected code and reproduced where possible. The value of this connection is the evidence it brings into that process.
Playwright MCP
Our pick for giving an existing agent browser actions.
Playwright MCP exposes browser automation through MCP. It uses structured accessibility snapshots to help the agent understand pages and interact with them.
Try it on a local or test application: “Open the settings page, change this preference, reload, and verify that the new value persists.” Require the agent to report the observed result, not just the sequence of clicks it attempted.
This server supplies browser capabilities; the surrounding agent still decides what to do. Session setup and the chosen browser matter, particularly for authenticated tasks. For a comparison of the underlying approaches, see our browser automation tools guide.
Chrome DevTools MCP
Our pick when the question is why a page behaves badly.
Chrome DevTools MCP lets coding agents inspect a live Chrome browser, including network activity, console messages, screenshots, and performance traces.
Try: “Record a trace for this page load. Identify the largest delay and show the evidence before proposing a fix.” That is a different task from simply navigating a form successfully.
We would choose it for browser diagnosis and performance investigation. Check the documented browser requirements and data-collection controls before setup. Like other browser integrations, it can expose the content of the connected browser session to the agent, so choose the session deliberately.
Exa
Our pick for adding source discovery to a research agent.
Exa's MCP server brings its search capabilities into MCP-compatible clients. It is useful for work that needs information outside the team's connected apps.
Try: “Find original engineering write-ups about this architecture choice. Give me the source URLs and explain which question each source helps answer.” Start with a narrow research question so you can judge relevance.
Finding a promising result does not establish that its contents support the conclusion. Have the agent read the evidence and keep claims attached to sources. Our search API comparison covers the underlying search choices and published benchmarks in more detail.
Firecrawl
Our pick when the agent needs usable content from web pages.
Firecrawl MCP exposes web search, scraping, and related extraction capabilities to agents. It is worth considering when discovering a page is only the start of the work.
Try: “Read these three documentation pages and compare their export limits. Preserve the units, exceptions, and source links.” A structured output makes omissions easier to spot.
Check the extracted content against the original pages, especially when the answer depends on a table or footnote. Fetching more pages is not automatically better. Start with the URLs that can answer the question and expand when the evidence is incomplete.
Stripe
Our pick for agents assisting with billing work.
Stripe MCP connects AI tools to Stripe capabilities. It belongs in a billing workflow where the agent needs account context, rather than in every agent's default tool list.
Begin in test mode: “Inspect this test customer and explain the current subscription state. Show the records you used.” This lets you evaluate the agent's interpretation before considering actions that change billing data.
Choose the connection's available permissions deliberately and separate investigation from execution. A request to explain a subscription should not become an unrequested refund or cancellation. The useful result is a clear explanation tied to the actual records.
How to choose your first MCP servers
MCP standardizes how a client connects to capabilities. It does not make every server interchangeable or guarantee the quality of an agent's decisions.
Before adding one, answer four questions:
- What task does this unlock? Name an outcome, such as investigating an issue or checking documentation.
- What can the connected account access? Check account scope as well as the server's advertised features.
- Does your client support its setup? A hosted connection and a locally launched process have different requirements. Use the project's official setup guide.
- How will you verify the result? Look for source links, observed browser state, or records you can inspect.
Do not treat ten as an installation target. Add the smallest set that completes the task, then expand when you find a real gap.
Using MCP connections in Dexto
Dexto's Connections let you connect services and custom MCP servers. Use the documented connection flow and check the tools that become available. The MCP setup guide covers the other direction: connecting an external AI client to Dexto's own MCP server.
The server gives an agent access. A skill describes how to use that access for a repeatable job. For example, a release-review skill could tell the agent to read issues, inspect PRs, check evidence, and prepare a draft summary before changing anything.
That combination is where the value comes from: the right information, a useful set of actions, and a process you can review. Our guide to agent harnesses explains how those pieces fit together.