把 PDF / Word(.docx) / PPT(.pptx) / HTML / 纯文本 解析成干净 Markdown + 结构化（按实际解析页数计费，每页 1 credit）。
纯解析 + 格式转换、不生成任何 AI 内容；扫描件 OCR 暂未支持。
支持 page_range 只解析指定页码段（只按解析的页数扣费）。
| Name | Required | Description | Default |
|---|---|---|---|
| file_id | No | 已上传到文件中转站的文档 ID（与 data_base64 二选一） | |
| filename | No | 文件名带扩展名；base64 输入时必填以判类型（.pdf/.docx/.pptx） | |
| max_pages | No | 最多解析页数，0=全部 | |
| page_range | No | 页码范围，如 3-10 或 5，空=全部（PDF/PPT 有效） | |
| data_base64 | No | 文档内容 base64（与 file_id 二选一） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and delivers by disclosing billing (per-page credits), the pure-parsing nature, lack of OCR support, and page_range behavior. These details go well beyond a basic purpose statement and set accurate expectations.
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 compact, using three sentences to cover capabilities, limitations, and billing. Every sentence provides value, with no fluff or repetition.
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?
The tool has no output schema, and the description explains input formats, limitations, and billing, but it leaves the term 'structured' undefined. Given the moderate complexity and the existence of sibling tools, the description is sufficiently complete for an agent to select and invoke the tool correctly.
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 input schema already describes all 5 parameters with 100% coverage, so the baseline is 3. The description adds billing semantics tied to parsed pages, which enriches the meaning of max_pages and page_range beyond the schema's syntax-level details.
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 tool parses PDF/Word/PPT/HTML/plain text into clean Markdown and structured format, using a specific verb and listing supported file types. It distinguishes itself from sibling tools by emphasizing full-document parsing and conversion rather than AI content generation.
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?
It provides clear context about pure parsing (no AI content) and the OCR limitation, which tells users when not to use it. However, it does not explicitly mention alternatives like doc_chunk, doc_images, or doc_tables, so it falls short of a full usage guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct aspect of document processing: parsing to Markdown, extracting images, extracting tables, and chunking for RAG. The descriptions clearly differentiate their purposes, and doc_tables explicitly notes its difference from doc_parse.
All tools follow the consistent pattern 'doc_' + a noun (chunk, images, parse, tables). Naming is uniform, lowercase, and underscore-separated, making the tool surface predictable.
Four tools is a well-scoped number for a document parsing server, covering core parsing, image extraction, table extraction, and chunking without unnecessary redundancy.
The tool surface covers the full document processing pipeline: parse to Markdown, extract structured tables, extract images, and produce RAG-ready chunks. The only noted limitation (OCR) is explicitly disclosed and does not undermine the core workflows.