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

Annotations only declare readOnlyHint and openWorldHint. The description adds genuinely useful behavior beyond that: the returned fields (markdown, format, title, pages/sheets/slides, warnings), truncation behavior, table preservation, and the x402 pricing/free-tier model. This is substantial added context for a paid, open-world call.

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?

Two dense sentences, front-loaded with purpose and formats, then input modes, outputs, and cost. No filler; every clause carries information an agent needs.

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?

With no output schema, the description compensates by naming the return fields and warnings, and it covers formats, both input modes, truncation, and pricing. An agent has everything needed to call it correctly and anticipate the response.

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 baseline is 3. The description goes slightly further by framing the input modes as an either/or ('Send {url} or {base64, filename}'), clarifying the intended pairing of base64 with filename, which the flat schema does not express.

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?

Specific verb+resource ('Convert any document to clean Markdown') with an explicit enumeration of supported formats and the LLM-oriented goal. It is unmistakably distinct from siblings like json_tool, hash, or base64.

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 clearly conveys when to reach for this tool (document-to-Markdown for an LLM) and how to supply input ({url} or {base64, filename}). It stops short of naming an alternative tool or stating exclusions, but the sibling set has no overlapping converter, so the gap is minor.

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