Best MCP Servers for Cursor IDE in 2026
Why MCP + Cursor?
Cursor is the AI-first code editor that developers love. With Model Context Protocol (MCP) support, Cursor can connect to external tools and data sources - giving your AI assistant real capabilities beyond the chat window.
We picked 8 verified MCP servers from the Spark catalog that work great with Cursor.
1. Spark MCP Server
Search and discover AI tools directly from your editor. Find agents, prompts, MCP servers, and skills from a catalog of 4,500+ assets.
Why it's useful: When you need a tool for a specific task, ask your AI assistant to search Spark instead of leaving Cursor to browse the web.
npx evc-spark-mcp
Pros: 5 tools (search, get, content, popular, categories) · Works both local and remote
Cons: Read-only (can't publish from Cursor yet)
2. Extended Memory MCP Server
Persistent memory between sessions. Saves project context, architecture decisions, and preferences - isolated per project.
Why it's useful: Cursor loses context when you hit token limits or start new chats. This server remembers what you've discussed across sessions.
Pros: Automatic save/restore · Per-project isolation · Survives token limit resets
Cons: Requires local storage setup
3. GitHub Projects MCP Server
Full GitHub Projects management via GraphQL. Create items, update fields, manage workflows - all through natural language.
Why it's useful: Manage your project board without switching to the browser. "Move this issue to In Progress" just works.
Pros: Complete project management (items, fields, views) · GraphQL-based (fast)
Cons: Requires GitHub token with project scope
4. AI Tasks MCP Server
A task management system built for AI agents. Create plans, track progress, and manage tasks using Valkey as the persistence layer.
Why it's useful: When working on complex features, your AI can maintain a structured task list and track what's done vs. what's next.
Pros: Built for agentic workflows · Persistent via Valkey · Plan-based task organization
Cons: Requires Valkey/Redis instance running
5. API Lab MCP Server
Turn Cursor into an API testing lab. Test endpoints, validate responses, and debug APIs through conversation.
Why it's useful: Instead of switching to Postman, test your API directly: "Send a POST to /api/users with this JSON body and check the response."
Pros: Auth support · Response validation · Performance metrics
Cons: Not a full Postman replacement for complex collection workflows
6. Jupyter MCP Server
Connect AI to Jupyter Notebooks in real time - execute code, manipulate cells, view outputs including images and charts.
Why it's useful: Data analysis directly from Cursor. "Run this cell and show me the chart" without leaving your editor.
Pros: Real-time cell execution · Multimodal output (images, HTML) · Works with any Jupyter deployment
Cons: Needs a running Jupyter server
7. APIWeaver MCP Server
Dynamically creates MCP servers from any web API configuration. Point it at a REST API or GraphQL endpoint and get MCP tools automatically.
Why it's useful: Instead of writing custom MCP servers for each API, configure once and get tools for free.
Pros: Supports REST, GraphQL, any web service · No coding required · FastMCP-based
Cons: Complex APIs may need manual config tuning
8. Starwind UI MCP Server
AI-assisted Starwind UI component development. Get component documentation, props, and usage examples directly in Cursor.
Why it's useful: If you use Starwind UI, this server provides your AI with full component knowledge for accurate code generation.
Pros: Component-aware AI completions · TypeScript implementation
Cons: Only useful if you use Starwind UI
How to Install MCP Servers in Cursor
Add servers to your .cursor/mcp.json:
{
"mcpServers": {
"spark": {
"command": "npx",
"args": ["-y", "evc-spark-mcp"]
}
}
}
Or use the Spark one-click install: each asset page has a "Copy install command" button.
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