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Analyze PDFs with OCR, extract TOC, generate summaries, flashcards and quizzes
This server has significant gaps in definition quality. Tool naming follows verb-first conventions reasonably well (analyze_pdf, extract_toc, summarize_pages), but descriptions lack the depth needed for LLM tool selection, most are 40-80 chars, below the 50-200 char production baseline. Parameter schemas are present but inconsistent: some parameters use string types with enum-like descriptions (e.g., 'use_ocr': 'false'|'auto'|'true') instead of proper enum types, forcing LLMs to guess valid values. Critical parameters lack format constraints (page ranges accept free-form strings that must be parsed). Error handling is not visible in the schema definitions, and output schemas are entirely undocumented, LLMs cannot predict what these tools return. The server processes heavy ML workloads (summarization, flashcard generation, quizzes) but the definitions don't convey computational cost or latency expectations, which is critical for agent planning.
Analyze a PDF file and extract comprehensive information including text, structure, metadata, and optional OCR processing
Extract and save images from specified PDF pages with metadata
Extract text from PDF with optional OCR for scanned documents
Extract and parse the table of contents from a PDF document with intelligent hierarchy detection
Generate study flashcards from PDF content with question-answer pairs using NLP
Generate multiple-choice quiz questions from PDF content with answer keys
Generate abstractive summaries of specified PDF pages using transformer models
Output schemas are completely undocumented. LLMs cannot predict what these tools return, forcing them to guess field names and structure downstream. This violates pattern:tool and pattern:response-shaper.
Enum-like parameter values (e.g., use_ocr: 'false'|'auto'|'true', output_format: 'base64'|'files'|'metadata', difficulty: 'easy'|'medium'|'hard') are documented in descriptions but not declared as enum types in JSON Schema. LLMs must parse text to infer valid values, increasing hallucination risk.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 42 | <=2025-11-25 | v2 |
Tool descriptions are generic and lack differentiation. analyze_pdf, extract_text, and extract_toc all claim to 'extract' from PDFs, descriptions don't clearly explain when to use one vs. another, forcing LLMs to reason through ambiguity or make wrong tool selections.
No error handling or recovery guidance documented. What happens if a PDF is encrypted, corrupt, or has no extractable text? What if OCR fails? What if flashcard/quiz generation produces fewer cards than requested? LLMs have no fallback strategy.
Parameter format constraints are missing or implicit. Page ranges accept arbitrary strings (e.g., '1-5,10,15-20') but no regex or format is specified in schema. OCR DPI defaults to 200 but bounds are nowhere documented. max_file_size_mb is configured server-side but not exposed to LLM.
Inconsistent use_ocr parameter types across tools. analyze_pdf and extract_text use string ('false'|'auto'|'true'), while extract_toc and summarize_pages use boolean. This inconsistency forces LLMs to remember different calling conventions for conceptually similar features.
Missing idempotence guarantees. generate_flashcards and generate_quiz produce NLP-based outputs that may differ on repeated calls. No documentation on whether LLMs can safely retry or whether duplicate cards/questions will occur.
No documentation of computational cost or latency. summarize_pages, generate_flashcards, and generate_quiz load heavy transformer models but don't warn LLMs about timeouts, resource constraints, or expected duration. Agent planning cannot account for these costs.