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FastMCP server for Google's langextract library - extract structured information from unstructured text using LLMs
The server provides two well-intentioned tools with decent parameter coverage and clear high-level descriptions. However, there are significant gaps in parameter descriptions, output schema documentation, and error handling guidance. The tool names follow verb_noun convention appropriately (extract_from_text, extract_from_url), but parameter descriptions lack the granularity needed for robust LLM invocation. The rubric baseline shows 100% of A+ tools document return types; this server does not. Additionally, the schema includes complex nested objects (examples array with extraction items) that lack detailed type definitions in the visible schema. These gaps place the server in the 'fair' range, functional but notably incomplete.
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.
Extract structured information from text at a URL using langextract. Downloads text from a URL and extracts structured information based on user-defined instructions and examples. Useful for web scraping with structured output.
Output schema not documented. Tool descriptions state what the tool does but do not describe the structure of returned extraction data. LLMs cannot plan downstream operations or validate extraction fields without knowing the response schema.
Parameter 'examples' lacks detailed type documentation. The schema indicates it is an array with 'extraction' fields, but the nested structure (extraction_class, extraction_text, attributes) is not formally defined. LLMs cannot reliably construct valid examples without explicit type and field descriptions.
No error handling guidance. Tool descriptions do not explain what errors can occur (invalid model_id, malformed examples, network timeouts for extract_from_url) or how the LLM should recover. Pattern baseline: 'Error responses must tell the LLM what to do next.'
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 59 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 25 | - | v1 |
Parameter descriptions lack actionable constraints. 'Sampling temperature between 0.0 and 1.0' is present, but max_char_buffer (1000 default) has no documented min/max bounds. Unbounded parameters let LLMs pass absurd values (e.g. max_workers=10000) that may fail or cause timeouts.
Parameter 'prompt_description' description is generic. 'Description of what to extract and how' does not explain format expectations, length limits, or whether it should reference the example structure. LLMs may pass vague prompts that fail extraction.
No pagination or result limits documented. Tools may return large extraction datasets. Rubric baseline: 'Even if the API allows returning thousands of items, cap results at a reasonable limit (20-50) and offer pagination.' No limit is stated in descriptions.