---
title: "Best MCP Servers to Build an AI Agent Stack (2026)"
description: "The six capabilities a production AI agent needs, the MCP server that covers each one, and three ready-made stacks for research, coding and internal assistants."
---
# Best MCP Servers to Build an AI Agent Stack (2026)

> The six capabilities a production AI agent needs, the MCP server that covers each one, and three ready-made stacks for research, coding and internal assistants.

Source: https://www.zenrows.com/blog/best-mcp-servers-for-ai-agent-stack

When building an AI agent, a single MCP server rarely suffices. You need to **figure out which combination of servers gives it the capabilities it requires**.

Your agent may need to search for information, retrieve data from websites, interact with pages, remember information across sessions, or work with a codebase. Even when the agent's job is as narrow as pulling data from the web, **the best web data infrastructure for these agentic AI workflows is still a stack**. One assembled around the capabilities you require.

This guide breaks down the six categories every production agent stack needs tool coverage for. They include web data access, browser automation, memory, code and dev tools, databases, and productivity and SaaS integrations.

**The short answer: the best MCP servers for an AI agent stack in 2026 are Zenrows MCP for web data and browser automation, Mem0 for memory, GitHub MCP for code, Supabase MCP for databases, and the official Google Drive, Notion, Slack and Atlassian servers for productivity. Most agents need only two or three of them.**

## The Best MCP Servers at a Glance

| Category | Best MCP server | What your agent gets | Skip it when |
| --- | --- | --- | --- |
| Web data access | Zenrows MCP | Pages, structured JSON, crawls and batch jobs, on protected and JavaScript-heavy sites | Your agent never needs information from the web |
| Browser automation | Zenrows MCP | A cloud browser your agent clicks, types and navigates in, with residential proxies and anti-bot bypass built in | Your agent only reads pages |
| Memory | Mem0 (Zep for facts that change over time) | Context that persists across sessions | Your agent is stateless |
| Code and dev tools | GitHub MCP, with Context7 for library docs | Repositories, issues, pull requests and Actions runs | Your agent doesn't touch code |
| Databases | Supabase MCP | Tables, SQL queries, migrations and logs for a Postgres project | Your agent has no database to work with |
| Productivity and SaaS | Google Drive, Notion, Slack, Atlassian | Company documents, wikis, conversations and tickets | Your agent has no workplace data to act on |

Each server on this list is first-party or officially maintained, actively developed, and covers a job no other server in the stack covers.

## What Categories Does an Agent Stack Need?

The following categories cover the capabilities an agent may need across its workflow.

- **Web data access:** Covers scraping, extracting, and structuring information from specific websites and online sources. Agents need this to pull facts from a source they don't already have.
- **Browser automation:** Lets an agent control a browser to navigate pages, interact with elements, and complete tasks that require page interaction. Agents need this for anything that involves clicking, filling forms, or logging in rather than just reading a page.
- **Memory:** Lets an agent retain useful context, preferences, or information across interactions. Agents need this so they don't lose track of what happened in earlier sessions.
- **Code and dev tools:** Covers working with code, documentation, repositories, and development workflows. Agents need this to work with real codebases and access current library documentation when building or modifying code.
- **Databases:** Lets an agent read and change the data behind an application, from table schemas to SQL queries and migrations. Agents need this when the answer or the fix lives in the database rather than the code.
- **Productivity and SaaS:** Connects an agent to services such as Slack, Notion, Google Drive, and other workplace applications. Agents need this to act on the tools a team already uses, instead of working around them.

![The best MCP server for each job: Zenrows MCP for web data and browser automation, Mem0 for memory, GitHub MCP and Context7 for code, Supabase MCP for databases, and Google Drive, Notion, Slack and Atlassian for productivity](/blog/_img/best-mcp-servers-best-server-per-category.png)

The breakdown below covers each category, starting with web data access, since getting reliable information from the web is a core requirement for many agents.

## Web Data and Scraping: Zenrows MCP

**Zenrows is the best web scraping API for AI agents.** In our [benchmark](/blog/best-web-scraping-api-for-ai-agents) of four scraping APIs over 800 requests, it succeeded on 100% of requests against the DataDome, Cloudflare and Akamai targets. Instead of building scraping logic into the agent, connecting it to [Zenrows MCP](/mcp) lets it retrieve web pages on demand.

**[The MCP](https://docs.zenrows.com/mcp/overview) provides scraping, extraction, crawling, batch processing, and browser automation tools.** It handles JavaScript rendering and protected sites through Adaptive Stealth Mode, which automatically selects the configuration for each request. Every request starts on the lightest setup and escalates only when a site requires it, and you pay only for the configuration that succeeds.

**Once connected, your agent automatically picks the Zenrows tool that fits the task.** Use scrape for a single-page retrieval, extract when the result needs to be structured data, [crawl tools](/products/crawl) to start from one URL and collect the pages behind it, [batch tools](/products/batch) for large URL sets, or [browser tools](/products/browser) for workflows that require interaction with a page.

**When your agent needs only specific fields, tell it which ones in plain English, such as a product's name and price.** Zenrows returns those target fields as structured JSON instead of the full page, at a fraction of the tokens a full page costs.

Zenrows MCP runs as a hosted server with OAuth sign-in and nothing to install, or locally through the `@zenrows/mcp` package. An agent can also pay per request on its own over x402 or MPP at [agents.zenrows.com](https://agents.zenrows.com/llms.txt), with no Zenrows account.

If you want a full comparison of [scraping-focused MCP servers](/glossary/ai-agents-and-the-web/what-is-a-web-scraping-mcp-server), see the [existing scraping MCP roundup](/blog/best-scraping-mcp-servers).

[Start building free](https://app.zenrows.com/register) with 5,000 credits every month, no credit card required.

## Browser Automation: Zenrows MCP

Some tasks need more than reading a page. Your agent has to log in, fill a form, click through pagination, or pick an option from a dropdown before the data it needs appears. **Zenrows MCP's [browser tools](/products/browser) give your agent control of a cloud browser with residential proxies and anti-bot bypass built in, so these interactions keep working on protected sites.**

Your agent opens a session on a URL and keeps working in it across calls, with its cookies, local storage, and tabs intact. It can click, type, fill and submit forms, select options, scroll, hover, drag, and press keys. To understand a page before acting on it, the agent reads its accessibility tree as text, so standard interactions work without a vision model. It can also take screenshots, save a page as a PDF, or run JavaScript in the page, and a single call can chain several of these actions together.

Because the browser runs in the cloud, there is nothing to install or keep updated on your machine, and the session sits next to Zenrows' scrape, extract, crawl, and batch tools in one MCP connection. Your agent can scrape a listing page, then open a browser session only for the pages that need a click or a login.

[Playwright MCP](/glossary/ai-agents-and-the-web/what-is-playwright-mcp), Microsoft's first-party MCP server, drives a browser on your own machine instead. It supports Chromium, Firefox, and WebKit in headed or headless mode, which suits testing an application you run locally, so it often sits in a coding agent's stack next to Zenrows MCP. For a hands-on setup, see our [Playwright MCP guide](/blog/playwright-mcp).

## Memory: Mem0

Memory MCP servers store data across separate sessions, so your agent can remember user preferences, past decisions, and ongoing project state.

Mem0 provides an MCP server that lets agents store and retrieve this information as part of their workflow. **It extracts relevant information from conversations and stores memories under scopes such as user, session, and agent.** This lets the agent retrieve what it needs without sending the full conversation history back into the context window.

When an agent corrects something, Mem0 can update the existing memory. The current official Mem0 MCP server is a [hosted cloud service](https://docs.mem0.ai/platform/mem0-mcp), so nothing runs on your machine.

The original self-hosted mem0-mcp repository is archived and no longer maintained. Mem0 itself remains open-source under the Apache 2.0 license, but self-hosting the MCP integration requires a community-built alternative rather than an official Mem0 server.

**Zep is worth considering if your agent needs to track how facts and relationships change over time.** Its Graphiti engine handles temporal relationships, which can help with compliance, audit trails, and evolving entity data.

A scenario where you don't need a memory server is when using a stateless agent. For example, a syntax checker that runs once and returns an answer doesn't need to remember previous sessions.

## Code and Development: GitHub MCP

GitHub MCP Server gives your agent structured access to GitHub's developer platform. As a first-party server with over 33k GitHub stars as of October 2026, it has become a prominent MCP server for code-aware agents.

**Your agent can search code, inspect branches, access issues and pull requests, and work with GitHub Actions workflow runs. This gives it access to the project's current state, including repository changes and open work, instead of relying on information from its training data.**

For example, suppose a GitHub issue reports that users receive a 500 error when they submit the login form. Your agent can inspect the issue, search the repository for the login handler, identify the relevant code, and fix it. It can then commit the change to the repository for review.

GitHub MCP handles repository access and operations, but code execution and testing still need to happen elsewhere. Your agent can retrieve files and make changes through GitHub MCP, while a CI/CD system can run the code and verify that the changes pass the project's tests.

For current library documentation, Context7 by Upstash is a common companion. It pulls up-to-date, version-specific docs and code examples into your agent's context, so the code it writes matches the library version you use.

## Databases: Supabase MCP

Many agents need the data behind an application, not just its code. A database MCP server lets your agent inspect table schemas, run SQL queries, and work with migrations, so it can answer questions about the data or fix a problem where it lives.

Supabase provides an official MCP server for its Postgres projects. It runs as a hosted server at `https://mcp.supabase.com/mcp`, where your client signs in to Supabase through the browser, so there is no access token to manage. **Your agent can list tables, run SQL, apply migrations, query logs, and check security and performance advisors, and it can also deploy Edge Functions and generate TypeScript types from your schema.**

You can limit the server to a single project and switch it to read-only mode, which runs queries as a read-only Postgres user. That combination suits an agent that analyzes production data without needing to change it.

## Productivity and SaaS

The best MCP servers for productivity plug your agent into the workspaces where your team's information and day-to-day work actually live.

These connections give your agent access to company knowledge that would otherwise stay inside documents, conversations, project trackers, and internal wikis.

Instead of working from information you have pasted into a prompt, your agent can connect directly to these platforms through official MCP servers and search, read, and update company data as needed.

The main platforms include:

- **Google Drive:** A common home for company documents, spreadsheets, and presentations. Your agent can search files, summarize documents, and pull data from spreadsheets without you having to export the files first.
- **Notion:** A workspace for wikis, project documentation, notes, and databases. The official Notion MCP server lets your agent search and retrieve information from your workspace. It can also work directly with pages and databases, reading and updating content as needed.
- **Slack:** Your agent can search messages and other workspace content, retrieve channel history and threads, and send messages directly in Slack. This is useful when the information it needs is buried in team conversations or scattered across documents and project trackers.
- **Atlassian:** Used for project management and technical documentation through tools such as Jira and Confluence. An Atlassian MCP connection lets your agent search and work with Jira tickets, access project information, and search internal documentation in Confluence.

These six categories cover the core capabilities an AI agent may need. The next step is to combine the servers that match your agent's workflow and decide which tools belong in the stack.

## Putting the Stack Together

**You do not need every MCP server in your agent. The right combination depends on what your agent needs to find, retrieve, remember, modify, or automate.** Let's observe some examples.

### Web Research Agent

For an agent that researches topics and works with current web information, a simple stack is:

- Web data: Zenrows
- Memory: Mem0

Zenrows retrieves content from JavaScript-heavy and protected websites, crawling a site when the agent needs more than one page, and Mem0 keeps useful context across sessions. You can leave memory out if the agent only handles one-off research tasks. For working builds, see this [multi-agent web research system with AG2 and Zenrows](/blog/web-research-multi-agent-ag2-zenrows) and how to [give Ollama agents live web access with Zenrows MCP](/blog/ollama-agents-live-web-access-zenrows-mcp).

### Coding Agent

For an agent that works with repositories and web applications, you can combine:

- Code and development: GitHub MCP
- Browser automation: Playwright MCP
- Web data: Zenrows

GitHub MCP covers the repository, including source code, issues, and pull requests. Playwright MCP comes in when the agent needs to interact with the application in a browser, and Zenrows can help your agent retrieve current documentation or API information from JavaScript-heavy or protected websites when it needs that information to complete a coding task. If your coding agent runs in a terminal, the [Zenrows integration for Claude Code](/integrations/claude-code) installs Zenrows' agent skills and prints the MCP config with one CLI command.

### Internal Company Assistant

For an agent that works with internal company information, a different combination makes more sense:

- Memory: Mem0
- Productivity and SaaS: Slack
- Productivity and SaaS: Google Drive
- Productivity and SaaS: Notion

The agent can pull information from Slack, Drive, and Notion, while Mem0 keeps useful context between sessions.

![Three sample agent stacks: a web research agent with Zenrows and Mem0, a coding agent with GitHub MCP, Playwright MCP and Zenrows, and an internal company assistant with Mem0, Slack, Google Drive and Notion](/blog/_img/best-mcp-servers-sample-agent-stacks.png)

## How to Add MCP Servers to Claude Code, Cursor and Other Clients

MCP servers connect to your agent in one of two ways. A remote server runs on the provider's infrastructure: you add its URL to your client and sign in with OAuth, so there is nothing to install or update. A local server runs on your machine as a subprocess, usually started with `npx` or Docker, and reads its credentials from environment variables.

Most clients, including Claude Code, Cursor, VS Code, and Claude Desktop, accept both. You add each server once to the client's MCP config, and your agent can list and call its tools from then on.

For Zenrows, the remote server is `https://mcp.zenrows.com/mcp`, and the local one runs with `npx -y @zenrows/mcp`. You can start the local server without an API key: on its first call, it creates a free account that you can claim later. The [Zenrows CLI](/cli) prints the exact config for your client, and the [CLI docs](https://docs.zenrows.com/cli/mcp) list every supported client.

## Common Mistakes When Combining MCP Servers

- **Too many tools:** Every connected server adds tool definitions to your agent's context. Past a point, the agent spends tokens on tools it never calls and picks the wrong one more often. Connect the servers your workflow uses and leave the rest off.
- **Overlapping servers:** Two servers that do the same job, such as two browser servers, make your agent choose between near-identical tools. Pick one server per category.
- **Broad credentials:** A GitHub token with write access to every repository, or a Slack connection that can post in any channel, gives your agent more reach than its task needs. Scope each token to the repositories, channels, and actions the workflow requires, and use read-only access where the agent only reads.
- **No approval step on writes:** An agent that reads outside content and can also send messages or push code can be steered by instructions hidden in that content, a risk known as prompt injection. Keep a human approval step on actions that write or send.

## Start With Your Agent's Workflow

An MCP stack works best when its servers support different parts of your agent's workflow. Web data tools can retrieve the information your agent needs, and other integrations can connect your agent to the systems where it needs to use that information.

The workflow determines the MCP connections your agent needs. Start with the tasks your agent must complete, then choose the servers that provide the tools and access it needs.

For the web data layer, Zenrows MCP gives your agent protected web access, structured output, crawling, batch jobs, and a cloud browser through one connection. [Start building free](https://app.zenrows.com/register) with 5,000 credits every month, no credit card required.

## FAQ

### What MCP Servers Does an AI Agent Actually Need?

An AI agent only needs the MCP servers required for the tasks it performs. A research agent might need Zenrows for web data, plus Mem0 for memory. A coding agent might need GitHub MCP and Playwright MCP instead. Start with the capabilities your agent needs and add servers only when they provide something missing.

### What Web Scraping API Has the Best MCP Integration?

Zenrows provides a native MCP server for retrieving web data, with tools to scrape a page, extract structured JSON, crawl a site, run batch jobs, and drive a cloud browser, all with anti-bot bypass built in. It runs as a hosted server at `https://mcp.zenrows.com/mcp` with OAuth sign-in, or locally through `@zenrows/mcp`, so your agent brings web data into its workflows without a separate scraping integration.

### Does Zenrows Have an MCP Server for AI Agent Integration?

Yes. Zenrows provides a native MCP server that connects AI agents to its web data capabilities. Connect the hosted server at `https://mcp.zenrows.com/mcp` with OAuth and nothing to install, or run `npx -y @zenrows/mcp` locally. Your agent then picks between five tool groups on its own: scrape, extract, crawl, batch, and browser.

### What Is the Best MCP Server for Browser Automation?

Zenrows MCP is the best MCP server for browser automation on live websites. Its browser tools give your agent a cloud browser with residential proxies and anti-bot bypass built in, so it can log in, fill forms, click, and scroll on protected sites, with scrape, extract, crawl, and batch tools in the same connection.

### What Is the Difference Between Zenrows MCP and Playwright MCP?

Both control a browser. Zenrows MCP drives a cloud browser with built-in residential proxies and anti-bot bypass, alongside its scraping, extraction, crawling, and batch tools, so your agent works on protected sites with nothing to install. Playwright MCP drives a browser on your own machine, which suits testing an application you run locally. They can work alongside each other in the same stack.

### What Are the Best MCP Servers for Claude Code?

For coding work in Claude Code, start with GitHub MCP for repositories, issues, and pull requests, Context7 for current library documentation, Playwright MCP to test your app in a browser, and Supabase MCP if your app's data lives in Supabase. Add Zenrows MCP when the agent needs documentation, API references, or other data from live and protected websites.

### What Are the Most Popular MCP Servers?

Among the servers in this guide, the most-starred on GitHub as of October 2026 are Context7 (more than 63k stars), Playwright MCP (more than 38k), and GitHub MCP (more than 33k).

### Should I Use Remote or Local MCP Servers?

Remote servers run on the provider's infrastructure and sign in with OAuth, so there is nothing to install or update. Local servers run on your machine as a subprocess and fit tools that need local access, such as Playwright MCP driving a browser on your desktop. Zenrows MCP offers both, with the same tools on each.

### How Many MCP Servers Should an Agent Have?

There is no fixed number. Use enough servers to provide the capabilities your agent needs. Just avoid servers that duplicate functionality or expose tools the agent will never use.