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Document extraction and RAG-enhanced processing service using Google Cloud Document AI for key-value pair extraction, entity recognition, and table extraction from PDFs and images, with multi-page document indexing for query capabilities
DocuExtract exhibits significant definition quality issues across multiple dimensions. Tool descriptions are present but often lack depth and specificity needed for LLM-driven decision-making. Parameter schemas exist but are minimally descriptive, with many parameters having single-word descriptions ('Document ID to process', 'User question to query against documents') that fall below the 72-character baseline for production tools. Most critically, NO tools include output schemas, LLMs cannot predict what structure will be returned, forcing blind usage. Error handling guidance is absent entirely. The server uses verbs appropriately (list, upload, process, get, delete, query) but parameter descriptions are far too terse to guide an LLM's reasoning, and several tools expose implementation details (processor_id, n_results defaults) without explaining their semantic significance. Overall composition is sound (single responsibility per tool), but execution falls well short of production readiness.
Delete a document and its file
Get document details including extracted data
Get the extracted data for a specific document.
List all uploaded documents
Process a document using Document AI and extract key-value pairs, entities, and tables.
Process a document: Single-page docs extract key-value pairs only. Multi-page docs extract key-value pairs and add to RAG for querying.
Query documents using RAG (Retrieval-Augmented Generation)
NO output schemas documented for any tool. LLMs cannot predict return structure. This violates pattern:tool requirement that output schema must be documented so LLMs can plan downstream calls.
Parameter descriptions are uniformly terse (1-2 words). 'Document ID to process', 'User question to query against documents' fall far below the 72-character baseline for production tools. LLMs cannot infer semantic meaning or constraints from stub descriptions.
upload_documents parameter 'files' schema lists type 'array' with items type 'object' and description 'UploadFile', but no further detail. LLMs cannot understand what fields UploadFile should contain or format.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 44 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 40 | - | v1 |
Get RAG system statistics
Upload one or more documents for text extraction. Saves files to local storage and records metadata.
delete_document tool is destructive but has no confirmation or dry-run capability. Tool description does not explicitly warn that deletion is irreversible. Violates pattern:confirmation-request.
process_document 'processor_id' parameter is optional with vague description 'Optional processor ID (uses default if not provided)'. No enum, no valid values, no guidance on what processor_id means or where to find one.
rag_query_endpoint 'n_results' defaults to 3 without documenting valid range or rationale. No description of what 'results' means (documents? chunks? statements?) or format of returned results.
No error handling guidance in any tool description. LLMs cannot know: is a 'document not found' error retryable? Can I search differently? Should I ask the user? Violates pattern:recovery-guide.
Tool naming uses '_endpoint' suffix (process_document_endpoint, rag_query_endpoint) which is implementation detail leakage. Should be 'process_document' and 'query_documents_with_rag' or similar, endpoint terminology is irrelevant to LLM planning.
process_document 'file_content' parameter documented as 'The document file content as bytes' but parameter type is 'string'. Mismatch between type and description. LLMs may try to pass binary data in a string parameter.
No parameter validation rules documented. Constraints like document_id format, file size limits, query length limits, or processor_id valid values are not specified. LLMs will pass invalid inputs blindly.