stagenth · 文档生成

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions the credit cost (2 credits), local conversion (no AI), automatic refund on failure, and the output destination (transfer station with URL return). However, it lacks details on file size limits or response format, leaving a minor gap.

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 appropriately concise, with the core action and cost in the first sentence and supplemental details in the second. It is front-loaded, avoids redundancy, and every sentence adds value.

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?

For a simple tool with three parameters, no annotations, and no output schema, the description covers essential aspects: what it does, cost, refund behavior, and supported Markdown structures. While it doesn't cover every edge case, it is sufficiently complete for a straightforward conversion 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?

Schema description coverage is 100% for all three parameters, so the schema already documents the parameters. The description adds general capability context (e.g., covers headings, lists, code blocks) but does not enhance specific parameter semantics beyond what is in the schema, warranting a baseline score.

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 explicitly states the tool renders Markdown into Word (.docx), saves it to a transfer station, and returns a download URL. This clearly identifies the function and distinguishes it from sibling tools like md_to_pdf and md_to_pptx by specifying the Word output format.

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 clear context for using the tool (Markdown to Word conversion, local processing, no AI content) but does not explicitly state when not to use it or mention alternatives by name. The sibling tool names imply different output formats, making the intended usage fairly obvious.

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

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

No annotations are provided, so the description carries full responsibility. It discloses significant behavioral traits: cost (2 credit/次), local rendering via markdown + weasyprint, no AI content generation, automatic refund on failure, and Chinese display support. These details go well beyond a simple 'convert Markdown to PDF' statement.

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 two sentences, front-loaded with the core function and immediately followed by essential details (cost, local rendering, refund policy). Every phrase serves a purpose and there is no redundant information.

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?

For a simple conversion tool with 3 parameters (one required), no output schema, and no annotations, the description covers purpose, output (download URL), cost, failure behavior, and technical constraints. It is complete enough for an agent to understand what to expect and when to use it.

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% with each parameter (title, filename, markdown_text) having a meaningful description. The tool description does not add any parameter-specific details beyond the schema, so it meets the baseline but does not exceed it.

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 '把 Markdown 渲染成 PDF 文件' (render Markdown to PDF), specifying the action, input type, and output format. It also mentions the result is stored in a file transfer station with a download URL, and the sibling tools (md_to_docx, md_to_pptx) make the format-specific purpose 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 clear context by stating it's a local renderer that doesn't generate AI content, implying it's for converting existing Markdown rather than content creation. However, it does not explicitly name alternatives or state when-not-to-use, though the sibling tool names provide implicit differentiation.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses key behaviors: pure local rendering ('纯本地渲染'), zero external cost, 1 credit per use, automatic refund on failure, and that output is placed in a file transfer station returning a download URL. This is substantial context beyond just 'converts Markdown to PPT'.

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 two sentences long, front-loaded with the essential Markdown mapping and followed by concise value propositions (cost, local rendering, refund). Every sentence adds value, with no redundant or filler content.

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?

For a simple 3-parameter tool with no output schema and no annotations, the description is complete: it explains input format, output behavior (download URL), cost, and failure handling. It provides enough information 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 has 100% description coverage: each parameter is fully documented (title overrides #, filename excludes extension, markdown_text explains syntax). The description merely echoes the same semantics without adding new parameter-level details, so it meets the baseline but does not exceed it.

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's purpose: '把 Markdown 渲染成 PPT（.pptx）' and provides the exact Markdown-to-slide mapping (# cover, ## pages, lists as bullets). It also mentions the output behavior (file transfer station + download URL), which differentiates it from sibling tools md_to_docx and md_to_pdf by targeting .pptx output.

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 a clear usage context: 'AI 写好大纲一步出片' (AI writes an outline and produces a presentation in one step), implying this tool is best for quickly turning an outline into slides. However, it does not explicitly state when to prefer this over md_to_docx or md_to_pdf, nor does it provide exclusions, so it falls short of a perfect score.

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