Analyse a market from scraped markdown

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

No annotations exist, so the description carries the whole burden; it usefully discloses that input must be Fiverr search-page markdown and that output is derived analysis ('how reachable a new seller is'), and that it does no scraping. It does not say how malformed/partial markdown is handled, whether the computation is deterministic, or how the threshold influences the verdict, leaving gaps for a tool with no annotation coverage.

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 sentences, no filler, with the input requirement, the computed outputs, and the intended use all front-loaded. Every clause earns its place.

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 an output schema present, return values need no explanation, and the description covers purpose, input expectations and usage context. The one meaningful omission is the review-threshold parameter's role in the verdict, but otherwise the definition is complete for a low-complexity, side-effect-free analysis 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 coverage is 0% and the description rescues one of two parameters by specifying the expected content and format of 'markdown' (raw scraped Fiverr search-page markdown as a string), which the bare 'string' schema does not convey. The second parameter, open_market_review_threshold, is entirely unexplained despite directly shaping the 'open market' verdict, so compensation is only partial.

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 concrete verb and resource ('Take raw Fiverr search-page markdown ... compute pricing bands, review-count distribution and an open market verdict') and names its outputs, so an agent knows exactly what it produces. Sibling differentiation is only implied ('without re-scraping' hints it does not fetch, unlike list_gigs/price_gig) rather than stated, which keeps it short of a 5.

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?

'Use for competitive analysis without re-scraping' gives a clear context and a reason to prefer this over re-fetching, which is implied guidance. It never names the sibling tools as alternatives or states when not to use it (e.g. when you have no markdown yet), so usage remains inferred rather than explicit.

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