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Cursor Setup

Connect Pipecat Context Hub to Cursor as an MCP server over stdio.

Prerequisites

  • Python 3.11+
  • Cursor installed
  • uv package manager

Build the Local Index

Build the local index before serving. The first run downloads the package, models, and sources — allow a few minutes:

uvx pipecat-ai-context-hub refresh

This populates ~/.pipecat-context-hub/.

Configure

Option A: Project-level config (recommended)

Create .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "pipecat-context-hub": {
      "command": "uvx",
      "args": ["pipecat-ai-context-hub", "serve"],
      "env": {}
    }
  }
}

Option B: Global config (all projects)

Create or edit ~/.cursor/mcp.json (same format as above).

Verify

  1. Open your project in Cursor.
  2. Open Cursor Settings > MCP to confirm pipecat-context-hub appears and shows a green status.
  3. In the AI chat, ask a question about Pipecat — the server's tools should be invoked automatically.

You can also verify the server starts correctly from the command line:

uvx pipecat-ai-context-hub serve --help

Troubleshooting

  • Server not appearing: Ensure .cursor/mcp.json exists in your project root directory.
  • Command not found: Ensure uv is installed and on your PATH (uvx ships with uv).
  • Empty results: Run uvx pipecat-ai-context-hub refresh to populate the index.
  • Red status indicator: Check the Cursor MCP logs for error details.