LangExtract MCP Server

# Supported Language Models This document provides comprehensive information about the language models supported by the langextract-mcp server. ## Currently Supported Models The langextract-mcp server currently supports **Google Gemini models only**, which are optimized for reliable structured extraction with schema constraints. ### Gemini 2.5 Flash - **Provider**: Google - **Model ID**: `gemini-2.5-flash` - **Description**: Fast, cost-effective model with excellent quality - **Schema Constraints**: ✅ Supported - **Recommended For**: - General extraction tasks - Fast processing requirements - Cost-sensitive applications - **Notes**: Recommended default choice - optimal balance of speed, cost, and quality ### Gemini 2.5 Pro - **Provider**: Google - **Model ID**: `gemini-2.5-pro` - **Description**: Advanced model for complex reasoning tasks - **Schema Constraints**: ✅ Supported - **Recommended For**: - Complex extractions - High accuracy requirements - Sophisticated reasoning tasks - **Notes**: Best quality for complex tasks but higher cost ## Model Recommendations | Use Case | Recommended Model | Reason | |----------|------------------|---------| | **Default/General** | `gemini-2.5-flash` | Best balance of speed, cost, and quality | | **High Quality** | `gemini-2.5-pro` | Superior accuracy and reasoning capabilities | | **Cost Optimized** | `gemini-2.5-flash` | Most cost-effective option | | **Complex Reasoning** | `gemini-2.5-pro` | Advanced reasoning for complex extraction tasks | ## Configuration Parameters When using any supported model, you can configure the following parameters: - **`model_id`**: The model identifier (e.g., "gemini-2.5-flash") - **`max_char_buffer`**: Maximum 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`**: Maximum parallel workers (default: 10) ## Limitations - **Provider Support**: Currently supports Google Gemini models only - **Future Support**: OpenAI and local model support may be added in future versions - **API Dependencies**: Requires active internet connection and valid API keys ## Schema Constraints All supported Gemini models include schema constraint capabilities, which means: - **Structured Output**: Guaranteed JSON structure based on your examples - **Type Safety**: Consistent field types across extractions - **Validation**: Automatic validation of extracted data against schema - **Reliability**: Reduced hallucination and improved consistency This makes the langextract-mcp server particularly reliable for production applications requiring consistent structured data extraction.