tecton-mcp

Tecton MCP Server & Cursor Rules

Tecton's Co-Pilot consists of an MCP Server and Cursor rules. Read this blog to learn much more.

ℹ️ Info: This guide will walk you through setting up the Tecton MCP server with this repository and configuring your feature repository to use it while developing features with Tecton.

Table of Contents

• Quick Start
• Tecton MCP Tools
• Architecture
• Setup Tecton with Cursor
• Updating the Tecton MCP Server
• How to Use Specific Tecton SDK Version
• Troubleshooting
• Resources

Quick Start

1. Clone this repository to your local machine:

   git clone https://github.com/tecton-ai/tecton-mcp.git
   cd tecton-mcp
   pwd
   

   Note: The path to the directory where you just cloned the repository will be referred to as <path-to-your-local-clone> in the following steps. The pwd command in the end will tell you what the full path is.

2. Install the uv package manager:

   brew install uv
   

3. Verify your installation by running the following command. Replace <path-to-your-local-clone> with the path where you cloned the repository in step 1:

   MCP_SMOKE_TEST=1 uv --directory <path-to-your-local-clone> run mcp run src/tecton_mcp/mcp_server/server.py
   

   The command should exit without any errors and print a message similar to MCP_SMOKE_TEST is set. Exiting after initialization.. This confirms that your local setup works correctly—Cursor will automatically spawn the MCP server as a subprocess when needed.

4. Configure Cursor (or any other MCP client) with the MCP server (see below)

5. Log into your Tecton cluster:

   tecton login yourcluster.tecton.ai
   

6. Launch Cursor and start developing features with Tecton's Co-Pilot in Cursor!

Tecton MCP Tools

The Tecton MCP server exposes the following tools that can be used by an MCP client (like Cursor):

ℹ️ Feature Services: If the MCP server is configured with a TECTON_API_KEY environment variable, the MCP server will register Tecton Feature Services as tools. This makes it possible for agents to query online feature services for fresh features from batch, streaming and real-time data sources.

Architecture

The Tecton MCP integrates with LLM-powered editors like Cursor to provide tool-based context and assistance for feature engineering:

The overall flow for building features with Tecton MCP looks like:

Setup Tecton with Cursor

The following is tested with Cursor 0.48 and above

Configure the Tecton MCP Server in Cursor

Navigate to Cursor Settings -> MCP and click the "Add new global MCP server" button, which will edit Cursor's mcp.json file. Add Tecton as an MCP server. You can use the following config as a starting point - make sure you modify the path <path-to-your-local-clone> to match the directory where you cloned the repository:

{
    "mcpServers": {
        "tecton": {
            "command": "uv",
            "args": [
                "--directory",
                "<path-to-your-local-clone>",
                "run",
                "mcp",
                "run", 
                "src/tecton_mcp/mcp_server/server.py"
            ]
        }
    }
}

Add Cursor rules

Symlink the cursorrules from this repository's [. cursor/rules folder](https://github.com/tecton-ai/ tecton-mcp/tree/main/.cursor/rules) into your feature repository. Using symlinks ensures that any updates to the original rules will automatically be picked up in your feature repository:

# Symlink the entire .cursor directory in your feature repo
ln -s <path-to-your-local-clone>/.cursor <path-to-your-feature-repo>/.cursor

Tecton Login

Log into your Tecton cluster:

    tecton login yourcluster.tecton.ai

Recommended LLM

As of June 2025, the following is the stack ranked list of best performing Tecton feature engineering LLMs in Cursor; this list may evolve over time as new models are released:

• Claude Sonnet 4
• OpenAI o3
• Gemini 2.5 pro exp (03-25)

Verify that the Cursor <> Tecton MCP Integration is working as expected

To make sure that your integration works as expected, ask the Cursor Agent a question like the following and make sure it's properly invoking your Tecton MCP tools:

Query Tecton's Examples Index and tell me something about BatchFeatureViews and how they differ from StreamFeatureViews. Also look at the SDK Reference.

If no calls are made to Tecton MCP tools, you may need to restart Cursor or reload your Cursor window to ensure new tools are properly registered.

Start AI-Assisted Feature Engineering :-)

Now you can go to your Feature Repository in Cursor and start using Tecton's Co-Pilot - directly integrated in Cursor.

View this Loom to see how you can use the integration to build new features: https://www.loom.com/share/3658f665668a41d2b0ea2355b433c616

Updating the Tecton MCP Server

To update the Tecton MCP server to the latest version:

1. Pull the latest changes from the repository:

   cd <path-to-your-local-clone>
   git pull
   

2. Close and restart Cursor to ensure the updated MCP server is loaded.

That's it! The MCP server runs as a subprocess spawned by Cursor, so there's no persistent background service to manually stop or restart. Cursor will automatically use the updated code the next time it needs to communicate with the MCP server.

How to Use Specific Tecton SDK Version

By default, this tool provides guidance for the latest pre-release of the Tecton SDK. If you need the tools to align with a specific released version of Tecton (for example 1.0.34 or 1.1.10), follow these steps:

1. Pin the version in pyproject.toml. Open pyproject.toml and replace the existing dependency line

dependencies = [
  # ... other dependencies ...
  "tecton>=0.8.0a0"
]

with the exact version you want, e.g.

dependencies = [
  # ... other dependencies ...
  "tecton==1.1.10"
]

2. Remove the existing lock-file. Because uv.lock records the dependency graph, you must delete it so that uv can resolve the new Tecton version:

cd <path-to-your-local-clone>
rm uv.lock

3. Re-generate the lock-file by re-running Step 3 (the MCP_SMOKE_TEST=1 uv --directory command) of the Quick Start section. (This will download the pinned version into an isolated environment for MCP and re-create uv.lock.)

4. Restart Cursor so that the new Tecton version is loaded into the MCP virtual environment.

Supported versions: The tools currently support Tecton ≥ 1.0.0. Code examples are not versioned yet – they always use the latest stable SDK – however the documentation and SDK reference indices will now match the version you've pinned.

Troubleshooting

Cursor <-> Tecton MCP Server integration

Make sure that Cursor shows "tecton" as an "Enabled" MCP server in "Cursor Settings -> MCP". If you don't see a "green dot", run the MCP server in Diagnostics mode (see below)

Run MCP in Diagnostics Mode

To debug the Tecton MCP Server you can run the following command. Replace <path-to-your-local-clone> with the actual path where you cloned the repository:

uv --directory <path-to-your-local-clone> run mcp dev src/tecton_mcp/mcp_server/server.py

Note: Launching Tecton's MCP Server will take a few seconds because it's loading an embedding model into memory that it uses to search for relevant code snippets.

Wait a few seconds until the stdout tells you that the MCP Inspector is up and running and then access it at the printed URL (something like http://localhost:5173)

Click "Connect" and then list tools. You should see the Tecton MCP Server tools and be able to query them.

Resources

• MCP Python SDK
• MCP Specification
• Blog about this implementation

License

This project is licensed under the .