Server Quality Checklist

Tool Scores

• monitor_public_awardsA
  4.8/5.0
  Behavior5/5

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

  Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond those: row flattening semantics, sorting by total award value, max_entities as a hard billing cap, data lag behavior, and domain resolution effects on speed. This significantly helps an agent predict side effects and limits.

  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 a single dense paragraph but every sentence carries useful information: output shape, register coverage, non-tender caveat, data lag, sorting/cap, auth, and read-only nature. It is front-loaded with the core behavior and then covers operational nuances. Slightly long but not wasteful; a 4 is appropriate.

  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?

  Given the tool's moderate complexity (7 parameters, no output schema), the description is impressively complete. It explains the return row structure, register sources, data freshness, sorting, row limits, auth/credits, and domain resolution behavior. No major context is missing 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.

  Parameters4/5

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

  The input schema already covers 100% of parameters with detailed descriptions, so the baseline is 3. The description adds extra semantic value by clarifying that max_entities is a billed-row hard cap, that resolve_domains speeds up runs ~20x, and that exclude_government_recipients is important for grant registers. These insights go beyond the schema descriptions, earning a 4.

  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 description clearly states what the tool does: it picks a public award register and time window and returns winning companies as flat rows. It lists the covered registers explicitly, which fully disambiguates the tool's scope. It also distinguishes from a tender feed, clarifying it reports already-made awards.

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

  Usage Guidelines5/5

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

  The description gives explicit when-to-use and when-not-to-use guidance, including that it is not a tender feed and will not show open bids. It provides practical timing advice (US data lags ~2 days, so avoid one-day windows) and warns about Apify credit consumption. It also notes the need for APIFY_TOKEN, which is essential operational guidance.

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