ingest_documents

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for behavioral transparency. It discloses that ingestion runs in a background job for async, explains backwards-compatibility flags (async_mode, use_marker), details the default extractor (PyMuPDF) and alternatives (Docling, with security holds), and warns about memory pressure with image-heavy textbooks. This is exceptionally transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Overview, ETL Pipeline, Args, Returns, Example). It is detailed yet concise, avoiding repetition. Every sentence adds value, and the use of bullet points and an example improves readability without sacrificing depth.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (11 parameters, no annotations, and a simple return type), the description is highly complete. It covers the workflow, key parameters, behavioral nuances, and provides a concrete example with follow-up status checking via get_job_status. This is sufficient for an agent to correctly invoke and monitor the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate. It provides detailed semantics for file_paths, async_mode, use_marker, marker_max_pages_per_chunk, extract_figures, and page_ranges. However, parameters like deskew, ocr_enabled, ocr_language, rotate_pages, and index_knowledge_graph are not described, leaving some ambiguity for these self-explanatory but still important options.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the primary function: 'Process PDF files and create Document Manifests.' It outlines the ETL pipeline steps (extract text/images, generate manifest, index in LightRAG). This distinguishes it from siblings like parse_pdf_structure (focused on structure parsing) and convert_document (likely format conversion), making its purpose specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage context with an example for async mode ('recommended for large files') and explains when to use the configured structured extractor (for precise source tracking). However, it does not explicitly differentiate from sibling tools or state when not to use this tool, leaving some room for interpretation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.