Docs in Your IDE: Cursor + Vectorize
![Graphic showing the Cursor logo above the Vectorize logo, with a plus sign between them, on a dark, abstract background with flowing purple and blue lines. Text reads “CURSOR + [vectorize]”](/_next/image?url=https%3A%2F%2Fmlrwd9rnffxq.i.optimole.com%2Fcb%3A641c.2be21%2Fw%3Aauto%2Fh%3Aauto%2Fq%3A90%2Ff%3Abest%2Fsm%3A0%2Fhttps%3A%2F%2Fblog.vectorize.io%2Fwp-content%2Fuploads%2F2025%2F09%2Fcursor-blog-post-1200x628-1.png&w=3840&q=75)
You’re midway through a PR review. Someone asks:
“What are the downstream dependencies of this service?”
Normally, you’d open three browser tabs, search the wiki, and dig through architecture diagrams. By the time you find the answer, your review flow is broken.
With Cursor connected to a Vectorize agent:
- Ask the question
- The agent scopes docs by service metadata
- You get the exact diagram and context — right inside your IDE
How It Fits Into Developer Work
You’re reviewing a pull request. A teammate flags a potential dependency issue, and instead of digging through diagrams, you pop open Cursor, type the question, and the service topology doc appears instantly — filtered to exactly the system in question.
Later that day, production throws a familiar error. Rather than combing through incident logs in a browser, you ask your agent for similar incidents in the last 30 days. The reports show up inline, scoped by severity, right where you’re already debugging.
When onboarding a new engineer, you no longer paste wiki links into Slack. They can just ask Cursor: “How do I set up the API gateway locally?” and the correct runbook steps surface in their editor.
Your flow doesn’t break. The answers come to you.
Quick Setup Recipe
Vectorize agents expose a structured search tool. Cursor connects to it via MCP and makes it available right in your command palette.
Step 1: Create a Vectorize Agent
Make an MCP agent in the Vectorize UI or API. It hosts your tools and handles retrieval over your docs.

Step 2: Add a Tool
Configure a tool and connect it to your pipeline. Set up parameters and link them to your pipeline’s metadata for automatic filtering.

Step 3: Configure Cursor’s MCP settings
Open Cursor Settings → MCP configuration and add:
{ "mcpServers": { "vectorize-mcp": { "url": "https://agents.vectorize.io/api/agents/YOUR_AGENT_ID/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY" } } } }
This tells Cursor to connect directly to your Vectorize MCP agent over HTTPS.
Step 4: Use It in Cursor
Restart Cursor. Your Vectorize agent will now appear in the Agent panel (⌘I
), accessible from the command palette.
Important: Cursor’s AI won’t automatically decide to use your MCP tool. You need to explicitly ask it.

Get Started
- Sign up for Vectorize
- Install Cursor
- Follow the integration guide
- Add your agent in Cursor Settings, restart Cursor, and start asking!