document_to_markdownReturn the document as structured Markdown (headings, tables, lists, code blocks, math).
Brainiall Doc Layout engine. The single API for converting documents to LLM-friendly format.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Base64-encoded PDF document | ||
| page_range | No | Page range like '1,2,5-10' or null for all pages |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds only 'Brainiall Doc Layout engine' and 'LLM-friendly format,' which is marginal behavioral context. There is no contradiction with the annotations.
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 first sentence is direct and informative, front-loading the core behavior and output format. The second sentence is short but largely branding-oriented; it adds mild context about LLM suitability but could be tighter. Overall the description is concise and well structured.
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?
Combined with the schema and annotations, the description gives the agent enough to understand the input (base64 PDF), optional page range, and output (structured Markdown). It lacks explicit routing among sibling tools and any note on limits or errors, but for a simple read-only conversion tool it is nearly complete.
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?
Schema description coverage is 100%, so the input schema already explains the base64 PDF and page_range parameters. The description does not add parameter-level semantics, but with full schema coverage the baseline of 3 is appropriate.
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 uses a clear verb ('Return') and resource ('the document') and specifies the exact output format: structured Markdown with headings, tables, lists, code blocks, and math. However, it does not explicitly differentiate this tool from sibling document tools like document_extract or document_tables, and the claim 'single API' is undercut by the existence of those siblings.
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 gives no concrete guidance on when to use this tool versus alternatives such as document_extract, document_tables, or document_query. 'The single API for converting documents to LLM-friendly format' is an assertion rather than usable routing guidance, and there are no exclusions or conditions stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct: document_* handle document analysis, while image tools (remove_background, restore_face, upscale_image) are unambiguous. However, document_extract and understand_content both perform field extraction from documents, differing mainly in schema flexibility, which could cause misselection. run_skillsets also overlaps conceptually as a pipeline tool.
Naming is partially consistent: image tools follow a verb_noun pattern (remove_background, restore_face, upscale_image), and document tools share a 'document_' prefix. However, the document tools mix noun_verb (document_extract, document_query) with noun_noun (document_tables) and document_to_markdown deviates with a preposition. This mixed convention reduces predictability.
With 10 tools, the count is well within the ideal 3-15 range. Each tool addresses a meaningful capability, from document parsing to image enhancement, without feeling redundant or excessive. The scope is appropriate for a multi-purpose image/document API.
The surface covers core workflows: document structuring (extract, markdown, tables, query), image enhancement (upscale, background removal, face restore), and health checks. Minor gaps include lack of explicit image format conversion or document deletion, but these are not essential for the stated purpose. Overall, the tools form a coherent set with no obvious dead ends.