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

Annotations mark this as non-read-only, non-destructive, open-world, so the mutation profile is covered. The description adds genuine context beyond that: credit consumption, an account prerequisite, and a latency-mitigation workflow. It does not describe rate limits or output format details, but with annotations carrying safety the added context is solid.

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?

Three sentences, front-loaded with the core purpose, then cost, then the fallback workflow. Every sentence carries non-redundant information with no filler.

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 5-param generation tool with full schema coverage and no output schema, the description supplies outputs, cost, auth, and the latency fallback. Nothing an agent needs to invoke it correctly is missing.

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 url/tone/length/transcript/include_embed all documented including enum semantics, so the schema does the heavy lifting. The description echoes the transcript parameter through the fallback workflow but adds no syntax or format detail beyond the schema. Baseline 3 applies.

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?

States a specific verb and resource ('Turn a YouTube URL into an article') and enumerates the concrete outputs (title, excerpt, HTML, Markdown), so an agent knows exactly what it produces. It is clearly distinguishable from siblings like get_youtube_transcript and generate_article_video.

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?

Gives explicit conditions and alternatives: it costs one credit, requires a free StartupHub.ai account, and if the call is slow the agent should call get_youtube_transcript first, then re-invoke with url and transcript. This is a named fallback with the triggering condition, not implied usage.

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