Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LANGEXTRACT_API_KEY | Yes | Your Gemini API key |
Schema
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| extract_from_text | Extract structured information from text using langextract. Uses Large Language Models to extract structured information from unstructured text based on user-defined instructions and examples. Each extraction is mapped to its exact location in the source text for precise source grounding. Args: text: The text to extract information from prompt_description: Clear instructions for what to extract examples: List of example extractions to guide the model config: Configuration parameters for the extraction Returns: Dictionary containing extracted entities with source locations and metadata Raises: ToolError: If extraction fails due to invalid parameters or API issues |
| extract_from_url | Extract structured information from text content at a URL. Downloads text from the specified URL and extracts structured information using Large Language Models. Ideal for processing web articles, documents, or any text content accessible via HTTP/HTTPS. Args: url: URL to download text from (must start with http:// or https://) prompt_description: Clear instructions for what to extract examples: List of example extractions to guide the model config: Configuration parameters for the extraction Returns: Dictionary containing extracted entities with source locations and metadata Raises: ToolError: If URL is invalid, download fails, or extraction fails |
| save_extraction_results | Save extraction results to a JSONL file for later use or visualization. Saves the extraction results in JSONL (JSON Lines) format, which is commonly used for structured data and can be loaded for visualization or further processing. Args: extraction_results: Results from extract_from_text or extract_from_url output_name: Name for the output file (without .jsonl extension) output_dir: Directory to save the file (default: current directory) Returns: Dictionary with file path and save confirmation Raises: ToolError: If save operation fails |
| generate_visualization | Generate interactive HTML visualization from extraction results. Creates an interactive HTML file that shows extracted entities highlighted in their original text context. The visualization is self-contained and can handle thousands of entities with color coding and hover details. Args: jsonl_file_path: Path to the JSONL file containing extraction results output_html_path: Optional path for the HTML output (default: auto-generated) Returns: Dictionary with HTML file path and generation details Raises: ToolError: If visualization generation fails |
| list_supported_models | List all supported language models and their characteristics. This server currently supports Google Gemini models only, optimized for reliable structured extraction with schema constraints. Returns: Dictionary containing model information and recommendations |
| get_server_info | Get information about the LangExtract MCP server. Returns server version, capabilities, and configuration information. Returns: Dictionary containing server information and capabilities |