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MCP Server

Connect Lucitra Validate to AI coding assistants and agent workflows using the Model Context Protocol (MCP). The Lucitra MCP server exposes validation tools that AI assistants can call directly, enabling natural language interaction with your synthetic data pipeline.
The MCP server uses the stdio transport and runs as a local process managed by your MCP client. No ports or network configuration are required.

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Installation

The MCP server is distributed as an npm package. No global installation is needed when using npx. 
npm install -g @lucitra/mcp
Package@lucitra/mcp
Server namelucitra-validate
Version0.1.0
Transportstdio

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Configuration

Add the following to your claude_desktop_config.json:
On macOS this file is located at ~/Library/Application Support/Claude/claude_desktop_config.json. On Windows it is at %APPDATA%\Claude\claude_desktop_config.json.
claude_desktop_config.json
{
  "mcpServers": {
    "lucitra-validate": {
      "command": "npx",
      "args": ["-y", "@lucitra/mcp"],
      "env": {
        "LUCITRA_API_KEY": "luci_your_api_key",
        "LUCITRA_API_URL": "https://api.lucitra.io"
      }
    }
  }
}
The LUCITRA_API_KEY and LUCITRA_API_URL environment variables are required. The server will fail to start without them.

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Available Tools

The MCP server exposes five tools that AI assistants can call.

validate_dataset

Run a full or targeted validation against a dataset. Polls until the job completes and returns the full report.

check_coverage

Run a coverage-only validation and return a focused summary with scores and details.

get_report

Fetch a previously generated validation report by its ID.

upload_dataset

Upload a local file as a new dataset. Creates the dataset record and handles the signed URL upload.

list_datasets

List all datasets in a project.

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validate_dataset

Run a validation job against a dataset. The tool polls for completion and returns the full report.
ParameterTypeRequiredDescription
datasetIdstringYesUUID of the dataset to validate
typeenumNoValidation type: coverage, physics, sim-to-real, full (default: full)
referenceDatasetIdstringNoUUID of a reference dataset for sim-to-real comparison

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check_coverage

Run a coverage-only validation and return a focused summary.
ParameterTypeRequiredDescription
datasetIdstringYesUUID of the dataset to check
Returns: 
{
  "coverage_score": 78,
  "overall_score": 82,
  "report_id": "rpt_a1b2c3d4e5f6",
  "details": {
    "scenario_coverage": 0.85,
    "class_coverage": 0.72,
    "edge_case_coverage": 0.68
  }
}

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get_report

Fetch a complete validation report by its ID.
ParameterTypeRequiredDescription
reportIdstringYesUUID of the report to retrieve

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upload_dataset

Upload a local file to create a new dataset. The tool creates the dataset record and uploads the file via a signed URL.
ParameterTypeRequiredDescription
projectIdstringYesUUID of the target project
namestringYesHuman-readable name for the dataset
formatenumYesDataset format: coco, kitti, nuscenes, custom
filePathstringYesAbsolute path to the local file

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list_datasets

List all datasets in a project.
ParameterTypeRequiredDescription
projectIdstringYesUUID of the project

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Example Prompts

Once the MCP server is configured, you can interact with Lucitra using natural language in your AI assistant. Here are example prompts: 
Validate my warehouse dataset ds_x7y8z9a0b1c2 and tell me if
the physics scores are above 85.

Run a coverage check on dataset ds_x7y8z9a0b1c2 and summarize
what scenarios are missing.

Run a sim-to-real validation on ds_abc123 using ds_def456 as the
real-world reference dataset.
The MCP server returns structured data, so your AI assistant can analyze scores, compare reports, and provide actionable recommendations without you needing to read raw JSON.

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Next Steps

CLI Reference

Use the CLI for scripting and CI/CD pipelines.

API Reference

Access the full REST API for programmatic integration.