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Cursor MCP Servers — Connect AI to Databases, APIs, and External Tools

Model Context Protocol servers extend Cursor's AI beyond your local codebase. An MCP server acts as a bridge between the AI model and an external system — your production database schema, a REST API specification, library documentation, a deployment pipeline, or a monitoring dashboard. When connected, the AI reads live data from these sources to generate code, queries, and configurations that are accurate to your actual infrastructure. No more guessing column names, API endpoint paths, or configuration values.

MCP is an open protocol supported by Cursor, Claude, and a growing ecosystem of development tools. Servers exist for PostgreSQL, MySQL, MongoDB, GitHub, Jira, Linear, Vercel, AWS, Docker, and dozens of other tools. You can also build custom MCP servers in TypeScript or Python — a typical server takes 50-200 lines of code. Configure MCP servers in Cursor settings and the AI gains access to external context in chat, Composer, and agent mode interactions.

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Cursor MCP server configuration panel showing connected database, API, and documentation servers

Cursor MCP Integration — April 2026

  • Model Context Protocol connects Cursor AI to databases, APIs, docs, and deployment tools
  • AI reads live data from external systems for context-aware code generation
  • Open protocol — build custom servers in TypeScript or Python with the MCP SDK
  • Available servers: PostgreSQL, MySQL, MongoDB, GitHub, Jira, Vercel, AWS, Docker, and more
  • Configure via Cursor Settings > MCP — supports stdio and SSE transport types
  • Works with chat, Composer, and agent mode for external context in all AI interactions
  • Available on Pro ($20/mo), Pro+ ($60/mo), Ultra ($200/mo), and Teams ($40/user/mo)

How MCP Servers Work in Cursor

An MCP server is a lightweight process that exposes tools and data to the AI. Cursor connects to the server, discovers available tools, and uses them during AI interactions when the context is relevant.

Cursor MCP server architecture diagram showing AI model connecting to external database via MCP protocol

Architecture — AI to External Tool via MCP

The Model Context Protocol defines a standardized communication layer between AI models and external tools. An MCP server exposes a set of tools — functions the AI can call — and resources — data the AI can read. When you ask the AI to "write a query that joins users and orders," the AI calls the MCP database server to read the actual schema, then generates a query with correct table names, column types, and join conditions. No hallucinated column names, no guessed data types.

MCP uses two transport protocols: stdio (standard input/output) for local processes and SSE (Server-Sent Events) for remote servers. Local MCP servers run as child processes of Cursor — they start when Cursor launches and stop when it closes. Remote servers run on your infrastructure and connect over HTTP. According to the Model Context Protocol specification, the protocol supports tool invocation, resource reading, prompt templates, and bi-directional communication between client and server.

Cursor settings MCP section with server name, transport type, and connection configuration fields

Setting Up an MCP Server

Open Cursor Settings and navigate to the MCP section. Click Add Server to configure a new connection. Provide a server name (for display in the AI tool list), select the transport type (stdio for local, SSE for remote), and enter the command or URL. For a PostgreSQL server, the command might be npx @modelcontextprotocol/server-postgres postgres://user:pass@host/db. For an API documentation server, provide the HTTP endpoint. Click Save and Cursor connects to the server immediately.

You can also configure MCP servers in your project's .cursor/mcp.json file for project-specific connections that travel with the repository. This is useful for team setups — commit the MCP configuration and every developer who pulls the repo gets the same external tool connections. The Cursor documentation provides step-by-step guides for configuring the most popular MCP servers including database, API, and documentation servers.

Cursor agent mode using MCP server to read database schema and generate a typed query with correct columns

Using MCP Context in AI Interactions

Once connected, MCP tools appear in the AI's available tool list. You do not need to explicitly invoke them — the AI automatically uses relevant MCP tools when the context matches. Ask "write a function that fetches all active users with their recent orders" and the AI calls the database MCP server to read the users and orders table schemas, then generates a query with the correct columns, types, and relationships. Ask "deploy the current branch to staging" and the AI uses the deployment MCP server to trigger the workflow.

MCP servers work across all AI features in Cursor. In chat, the AI uses MCP context to answer questions about your infrastructure. In Composer, MCP provides the schema information needed for accurate multi-file code generation. In agent mode, MCP servers give the agent access to external systems for verification and deployment. The combination of local codebase context (@codebase) and external tool context (MCP) gives the AI a complete picture of your development environment.

MCP Server Categories

The MCP ecosystem includes servers for every major tool category in modern development. Here are the most popular categories with example servers and their capabilities.

CategoryExample ServersWhat AI Can DoTransport
DatabasesPostgreSQL, MySQL, SQLite, MongoDBRead schemas, generate queries, create migrationsstdio
APIsREST endpoints, GraphQL, OpenAPIRead API specs, generate client code, test endpointsstdio / SSE
DocumentationLibrary docs, MDN, internal wikisFetch current docs for accurate code generationstdio / SSE
Source ControlGitHub, GitLab, BitbucketRead issues, PRs, commit history, create branchesstdio
Project ManagementJira, Linear, NotionRead tickets, update status, link PRs to issuesSSE
DeploymentVercel, Netlify, AWS, DockerTrigger deploys, read logs, check build statusstdio / SSE
MonitoringDatadog, Sentry, PagerDutyRead error reports, check metrics, diagnose issuesSSE
File SystemsS3, GCS, remote reposRead/write remote files, manage cloud storagestdio
SearchElasticsearch, Algolia, TypesenseQuery indexes, configure search, test relevancestdio / SSE
CustomInternal tools, proprietary APIsAny tool-specific operations you definestdio / SSE

Building Custom MCP Servers

The MCP ecosystem is open. Build servers for your internal tools, proprietary APIs, or any system the AI should access during code generation.

TypeScript SDK

The official @modelcontextprotocol/sdk TypeScript package provides a high-level API for building MCP servers. Define tools as functions with typed parameters and return values. The SDK handles protocol negotiation, transport management, and error handling. A minimal server that exposes one tool takes approximately 30 lines of code. Publish to npm for your team or keep it as a local script.

Python SDK

The mcp Python package mirrors the TypeScript SDK with Pythonic conventions. Use decorators to define tools and resources. The SDK supports both async and sync implementations. Build servers for Python-specific tools — Django management commands, SQLAlchemy introspection, FastAPI route documentation — with the same familiar Python patterns you use in your application code.

Common Custom Servers

Teams commonly build MCP servers for internal APIs (exposing endpoint documentation to the AI), proprietary databases (reading custom schema formats), configuration systems (fetching environment variables and feature flags), and deployment pipelines (triggering builds and reading logs). A custom MCP server turns any internal tool into an AI-accessible context source, eliminating the need to manually copy information into prompts.

Connect Cursor to Your Tools with MCP Servers

MCP servers give Cursor's AI live access to your databases, APIs, documentation, and deployment tools. Generate code with real schema data, test against actual API endpoints, and deploy from within the editor. Configure servers in Cursor settings or commit project-specific configurations to Git. Available on all paid plans. The MCP ecosystem grows daily with new servers for popular development tools.

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Frequently Asked Questions About Cursor MCP Servers

How MCP works, which servers are available, and how to build custom integrations for your tools.

What are MCP servers in Cursor?

MCP (Model Context Protocol) servers connect Cursor's AI to external tools — databases, APIs, documentation, deployment pipelines, and monitoring systems. The AI reads live data from these sources to generate context-aware code, queries, and configurations. No more guessing column names or API paths.

How do I set up an MCP server?

Open Cursor Settings > MCP, click Add Server, provide a name, select transport type (stdio or SSE), and enter the command or URL. Click Save and Cursor connects immediately. You can also configure servers in your project's .cursor/mcp.json file for team-shared configurations.

Which plans support MCP servers?

MCP servers are available on Pro ($20/mo), Pro+ ($60/mo), Ultra ($200/mo), and Teams ($40/user/mo). No per-server charge — AI operations using MCP context draw from your regular credit pool. The free Hobby plan does not include MCP support.

What types of MCP servers exist?

Servers exist for databases (PostgreSQL, MySQL, MongoDB), APIs (REST, GraphQL), documentation (library docs, wikis), source control (GitHub, GitLab), project management (Jira, Linear), deployment (Vercel, AWS), and monitoring (Datadog, Sentry). The ecosystem is open — build custom servers for any tool.

Can I build a custom MCP server?

Yes. Use the official TypeScript (@modelcontextprotocol/sdk) or Python (mcp) SDK. Define tools as functions, and the SDK handles protocol details. A minimal server is 30-50 lines of code. Common custom servers connect to internal APIs, proprietary databases, and company-specific tools.