LangExtract MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| extract_from_textA | 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 model_id: LLM model to use (default: "gemini-2.5-flash") max_char_buffer: Max characters per chunk (default: 1000) temperature: Sampling temperature 0.0-1.0 (default: 0.5) extraction_passes: Number of extraction passes for better recall (default: 1) max_workers: Max parallel workers (default: 10) Returns: Dictionary containing extracted entities with source locations and metadata Raises: ToolError: If extraction fails due to invalid parameters or API issues |
| extract_from_urlA | 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 model_id: LLM model to use (default: "gemini-2.5-flash") max_char_buffer: Max characters per chunk (default: 1000) temperature: Sampling temperature 0.0-1.0 (default: 0.5) extraction_passes: Number of extraction passes for better recall (default: 1) max_workers: Max parallel workers (default: 10) Returns: Dictionary containing extracted entities with source locations and metadata Raises: ToolError: If URL is invalid, download fails, or extraction fails |
| save_extraction_resultsA | 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_visualizationA | 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 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| README File | The README for the langextract-mcp server. |
| Supported Models | The supported models for the langextract-mcp server. |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap. extract_from_text and extract_from_url handle different input sources, generate_visualization creates visual outputs, and save_extraction_results handles persistence. The boundaries are well-defined and unambiguous.
All tools follow a consistent verb_noun pattern with clear, descriptive names. The naming convention is uniform throughout (extract_from_text, extract_from_url, generate_visualization, save_extraction_results), making the tool set predictable and easy to understand.
Four tools is well-scoped for a structured information extraction server. The tools cover the complete workflow: extraction from different sources, visualization generation, and result persistence. Each tool earns its place without redundancy or gaps in the core functionality.
The tool set provides complete coverage for the structured information extraction domain. It supports extraction from both text and URLs, visualization of results, and saving for later use. There are no dead ends or missing operations for the intended purpose.