Server Quality Checklist

Tool Scores

• track_publication_cadenceA
  4.4/5.0
  Behavior5/5

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

  The description goes far beyond the readOnlyHint and idempotentHint annotations. It explains why publication dates are read from post pages rather than sitemap lastmod (with a specific median lag), what happens when dates track edits (date_source_reliable=false and counts nulled), how counts can be a census or an even sample (counts_are_estimate), the requirement for APIFY_TOKEN, and that it consumes credits per domain. This is rich, non-obvious behavioral context that an agent needs to interpret results correctly.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is long but packed with unique value: every sentence introduces a caveat, metric, or constraint. It is front-loaded with the core purpose and then systematically covers reliability, counting methodology, and operational details (token, credits). While it could be trimmed slightly, the density is justified for a tool with no output schema and high behavioral nuance.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  With 6 parameters, no output schema, and no sibling tools, the description carries the full burden of explaining return values, edge cases, and operation constraints. It covers the returned fields (post counts, cadence_trend, format mix, bylines, discovery method), reliability flags, counting methodology, and prerequisites (APIFY_TOKEN). This is a complete picture for an agent to invoke the tool and interpret results correctly.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The schema description coverage is 100%, so the parameters are already well-documented. The description adds a bit of context (e.g., effect of max_pages_to_date on sampling, domain_time_budget_ms partial_result behavior) but mostly reinforces what the schema already states. The baseline of 3 is appropriate because the schema does the heavy lifting and the description does not introduce significant new semantics.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The first sentence states a specific verb and resource: 'measure how much long-form work that company publishes and whether the rate is rising or falling.' This clearly distinguishes the tool's purpose, even without sibling tools to compare, and immediately orients the agent to what it does.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly excludes product changelogs ('This measures EDITORIAL output volume, not product changelogs'), warns to check the date_source_reliable field before quoting numbers, and limits usage to 'Public sitemaps, feeds and pages only.' No alternatives are mentioned because no sibling tools exist, so the guidance is clear and provides actionable context.

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