Best MCP Server Integrations: ChatGPT, GitLab, Supabase, Slack & More

The Model Context Protocol has moved from experiment to infrastructure in under a year. Major platforms — GitLab, Supabase, Slack, Google, and many others — now ship official MCP servers. Your AI agent can query a Postgres database, post a Slack message, open a GitLab merge request, and write a row to a Google Sheet all from a single Claude session. The question is no longer whether to integrate these tools. It's which ones are worth your time and how to manage the chaos when you have a dozen of them running.

This guide covers the integrations that have the most practical signal for developers — based on real search demand — with honest notes on setup complexity and caveats.

ChatGPT / OpenAI MCP server

The most-searched MCP integration is also one of the most misunderstood. People searching for a "ChatGPT MCP server" are typically looking for one of two things: a way to connect Claude to OpenAI-compatible endpoints, or a way to use ChatGPT as an AI client that calls MCP tools.

OpenAI added MCP support in early 2025, meaning ChatGPT can now act as an MCP client — it can call tools exposed by any MCP server. This flips the usual framing: instead of connecting Claude to a tool, you expose your tool as an MCP server and any MCP-compatible client (Claude, ChatGPT, Cursor) can use it.

GitLab MCP server

GitLab's official MCP server lets your AI agent interact directly with your GitLab instance. You can create and triage issues, review and comment on merge requests, check pipeline status, and search the repository — all from natural language inside your AI client.

Setup requires a GitLab personal access token with the appropriate scopes. The server supports both GitLab.com and self-managed instances, making it one of the cleaner enterprise-grade integrations available.

{
  "mcpServers": {
    "gitlab": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-gitlab"],
      "env": {
        "GITLAB_PERSONAL_ACCESS_TOKEN": "glpat-...",
        "GITLAB_API_URL": "https://gitlab.com/api/v4"
      }
    }
  }
}

One practical note: if you're already using AgenticStore's create_pr and manage_issue tools for GitHub, the GitLab server covers the same use cases for GitLab-based teams.

Supabase MCP server

Supabase ships an official MCP server that gives your AI agent read and write access to your Supabase project. The agent can query tables with natural language, inspect row-level security policies, run database migrations, and manage Edge Functions.

This is particularly powerful for developers using AI to understand an unfamiliar schema or to generate type-safe queries against a production database. The agent can look up the actual schema rather than relying on stale documentation.

Slack MCP server

The Slack MCP server exposes your workspace to AI agents: read channels and threads, post messages, search conversation history, and manage workspace members. Common use cases include summarizing what happened in a channel while you were away, drafting and posting status updates, and searching Slack for context when debugging an incident.

Slack requires a Bot Token with specific OAuth scopes. The server runs locally and authenticates with Slack's API over HTTPS. Messages are not stored by the MCP server — they're fetched on demand.

Google Sheets and Google Drive MCP

Two closely related integrations. The Google Sheets MCP server lets your agent read and write spreadsheet data — query cells, write summaries, build reports from tool outputs. The Google Drive MCP gives broader access: create documents, list files, search by content, and share assets.

Both require a Google OAuth flow and a service account or user credentials. The Sheets integration is particularly useful for teams using spreadsheets as lightweight databases or dashboards — your agent can pull data, transform it, and write back results without copy-paste.

PostgreSQL MCP server

Direct SQL access for AI agents. The PostgreSQL MCP server — and its sibling for MySQL — lets agents query your database with natural language, inspect schema, explain query plans, and generate migrations. If your agent is doing anything with data, this is one of the most high-leverage integrations available.

Scope the connection string to a read-only user for anything touching production. AgenticStore's dependency_audit tool can also check that the Postgres client packages your project uses have no known CVEs.

Linear and Notion MCP servers

For teams using Linear for issue tracking, the Linear MCP server gives your agent create, update, and query access to your workspace. Agents can triage incoming bugs, update issue status based on git activity, and link related issues automatically.

Notion MCP follows the same pattern for knowledge management — query pages, create documents, and update databases. Useful for agents that need to reference internal runbooks or update documentation as part of an automated workflow.

The real problem: managing a dozen integrations

Each MCP integration requires its own config entry, its own API key or OAuth token, and its own server process. At two or three integrations this is manageable. At eight or ten, you're maintaining a dense JSON config, rotating keys across multiple services, and debugging "server disconnected" errors without good visibility into which server failed.

AgenticStore's GUI manager — the MCP Hub Webapp — was built specifically to address this. It runs on localhost, provides a visual dashboard for toggling tools on and off, stores API keys in your system keychain rather than plaintext JSON, and generates the correct client config for Claude, Cursor, and Windsurf automatically.