What is Groundlight Mcp Server

groundlight-mcp-server is a Model Context Protocol (MCP) server designed for interacting with Groundlight. It provides tools for creating and managing Detectors and ImageQueries.

Use cases

Use cases include automated image classification, real-time image querying for decision-making, and developing applications that require understanding and interpreting visual data.

How to use

To use groundlight-mcp-server, users can call various endpoints to create detectors, submit image queries, and retrieve results. The server requires specific inputs such as detector IDs and image data.

Key features

Key features include the ability to get and list detectors, submit image queries, retrieve existing queries and images, and create binary classification detectors that respond to natural-language queries.

Where to use

groundlight-mcp-server can be used in fields such as image processing, machine learning, and artificial intelligence, particularly in applications requiring image analysis and classification.

groundlight-mcp-server by

Overview

A Model Context Protocol (MCP) server for interacting with Groundlight. This server provides tools to create, list and customize Detectors, submit and list ImageQueries, create, list and delete Alerts, and examine detector evaluation metrics.

The functionality and available tools are subject to change and expansion as we continue to develop and improve this server.

Tools

The following tools are available in the Groundlight MCP server:

1. create_detector

   • Description: Create a detector based on the specified configuration. Supports three modes:

     1. Binary: Answers ‘yes’ or ‘no’ to a natural-language query about images.
     2. Multiclass: Classifies images into predefined categories based on natural-language queries.
     3. Counting: Counts occurrences of specified objects in images using natural-language descriptions.

     All detectors analyze images to answer natural-language queries and return confidence scores indicating result reliability. If confidence falls below the specified threshold, the query is escalated to human review. Detectors improve over time through continuous learning from feedback and additional examples.

   • Input: config (DetectorConfig object with name, query, confidence_threshold, mode, and mode-specific configuration)

   • Returns: Detector object

2. get_detector

   • Description: Get a detector by its ID.
   • Input: detector_id (string)
   • Returns: Detector object

3. list_detectors

   • Description: List all detectors associated with the current user.
   • Input: None
   • Returns: List of Detector objects

4. submit_image_query

   • Description: Submit an image to be answered by the specified detector. The image can be provided as a file path, URL, or raw bytes. The detector will return a response with a label and confidence score.
   • Input: detector_id (string), image (string or bytes)
   • Returns: ImageQuery object

5. get_image_query

   • Description: Get an existing image query by its ID.
   • Input: image_query_id (string)
   • Returns: ImageQuery object

6. list_image_queries

   • Description: List all image queries associated with the specified detector. Note that this may return a large number of results.
   • Input: detector_id (string)
   • Returns: List of ImageQuery objects

7. get_image

   • Description: Get the image associated with an image query by its ID. Optionally annotate with bounding boxes on the image if available.
   • Input: image_query_id (string), annotate (boolean, default: false)
   • Returns: Image object

8. create_alert

   • Description: Create an alert for a detector that triggers actions when specific conditions are met.
   • Input: config (AlertConfig object with name, detector_id, condition, and optional webhook_action, email_action, text_action, enabled, and human_review_required fields)
   • Returns: Rule object

9. list_alerts

   • Description: List all alerts (rules) in the system. (Note: Not filtered by detector in the current implementation.)
   • Input: page (integer, default: 1), page_size (integer, default: 100)
   • Returns: List of Rule objects

10. delete_alert

    • Description: Delete an alert (rule) by its alert ID.
    • Input: alert_id (string)
    • Returns: None

11. add_label

    • Description: Provide a label (annotation) for an image query. This is used for training detectors or correcting results. For counting detectors, you can optionally provide regions of interest.
    • Input: image_query_id (string), label (integer or string), rois (optional list)
    • Returns: None

12. get_detector_evaluation_metrics

    • Description: Get detailed evaluation metrics for a detector, including confusion matrix and examples.
    • Input: detector_id (string)
    • Returns: Dictionary of evaluation metrics

13. update_detector_confidence_threshold

    • Description: Update the confidence threshold for a detector.
    • Input: detector_id (string), confidence_threshold (float)
    • Returns: None

14. update_detector_escalation_type

    • Description: Update the escalation type for a detector. This determines when queries are sent for human review. Options: ‘STANDARD’ (escalate based on confidence threshold) or ‘NO_HUMAN_LABELING’ (never escalate).
    • Input: detector_id (string), escalation_type (string, either “STANDARD” or “NO_HUMAN_LABELING”)
    • Returns: None

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

Usage with Zed

Add this to your settings.json:

{
  "context_servers": {
    "groundlight": {
      "command": {
        "path": "docker",
        "args": [
          "run",
          "--rm",
          "-i",
          "-e",
          "GROUNDLIGHT_API_TOKEN",
          "groundlight/groundlight-mcp-server"
        ],
        "env": {
          "GROUNDLIGHT_API_TOKEN": "YOUR_API_TOKEN_HERE"
        }
      }
    }
  }
}

Development

Build the Docker image locally:

make build-docker

Run the Docker image locally:

make run-docker

[Groundlight Internal] Push the Docker image to Docker Hub (requires DockerHub credentials):

make push-docker

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