extract_from_url

extract_from_url

Extract structured data from web content using a URL. Downloads text and applies Large Language Models to retrieve specific entities based on provided instructions and examples.

Instructions

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

Input Schema

Name	Required	Description	Default
`config`	No
`examples`	Yes
`prompt_description`	Yes
`url`	Yes

Input Schema (JSON Schema)

{
  "$defs": {
    "ExtractionConfig": {
      "description": "Configuration for extraction parameters.",
      "properties": {
        "extraction_passes": {
          "default": 1,
          "description": "Number of extraction passes for better recall",
          "title": "Extraction Passes",
          "type": "integer"
        },
        "max_char_buffer": {
          "default": 1000,
          "description": "Max characters per chunk",
          "title": "Max Char Buffer",
          "type": "integer"
        },
        "max_workers": {
          "default": 10,
          "description": "Max parallel workers",
          "title": "Max Workers",
          "type": "integer"
        },
        "model_id": {
          "default": "gemini-2.5-flash",
          "description": "LLM model to use",
          "title": "Model Id",
          "type": "string"
        },
        "temperature": {
          "default": 0.5,
          "description": "Sampling temperature (0.0-1.0)",
          "title": "Temperature",
          "type": "number"
        }
      },
      "title": "ExtractionConfig",
      "type": "object"
    },
    "ExtractionExample": {
      "description": "Model for extraction examples.",
      "properties": {
        "extractions": {
          "description": "Expected extractions",
          "items": {
            "additionalProperties": true,
            "type": "object"
          },
          "title": "Extractions",
          "type": "array"
        },
        "text": {
          "description": "Example text",
          "title": "Text",
          "type": "string"
        }
      },
      "required": [
        "text",
        "extractions"
      ],
      "title": "ExtractionExample",
      "type": "object"
    }
  },
  "properties": {
    "config": {
      "$ref": "#/$defs/ExtractionConfig",
      "default": {
        "extraction_passes": 1,
        "max_char_buffer": 1000,
        "max_workers": 10,
        "model_id": "gemini-2.5-flash",
        "temperature": 0.5
      },
      "title": "Config"
    },
    "examples": {
      "items": {
        "$ref": "#/$defs/ExtractionExample"
      },
      "title": "Examples",
      "type": "array"
    },
    "prompt_description": {
      "title": "Prompt Description",
      "type": "string"
    },
    "url": {
      "title": "Url",
      "type": "string"
    }
  },
  "required": [
    "url",
    "prompt_description",
    "examples"
  ],
  "type": "object"
}

LangExtract MCP Server

Instructions

Input Schema

Input Schema (JSON Schema)

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MCP directory API