data_quality_fleet_rollup

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

Despite annotations already declaring readOnlyHint=true and destructiveHint=false, the description adds substantial behavioral context: it states 'Pure analysis,' details the exact return dict structure, explains deterministic 'now' for reproducibility, and clarifies that staleness budgets are per-tag/per-feed. This goes well beyond the annotations and provides valuable operational detail.

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?

The description is well-organized: a one-paragraph summary of the tool's purpose and behavior, followed by a structured Args list, Returns dict, and a concrete example. Every sentence adds value, and the length is appropriate for the tool's complexity. It is front-loaded with the core purpose.

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?

Given the tool's complexity and lack of output schema, the description is remarkably complete: it covers input structure, return fields, examples, and configuration nuances (slow vs. fast feeds). It also mentions deterministic 'now' for reproducibility, which is helpful for testing. No significant gaps remain.

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?

With 0% schema description coverage, the description fully compensates by providing an in-depth Args section that explains each parameter, including nested structure for 'feeds' with optional fields, the fallback behavior of 'default_staleness_s', the purpose of 'now', and the default for 'top_n'. An example usage further clarifies semantics.

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?

The description clearly states the tool's function: 'Cross-endpoint fleet rollup of data-TRUST: worst tags + bad quality.' It specifies a concrete verb ('rollup') and resource ('data-TRUST'), distinguishes itself from siblings like data_quality_scorecard by emphasizing a fleet-wide view, and lists distinct outputs (endpoint ranking, bad-quality rollup, liveness rollup).

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 provides strong contextual guidance: 'Builds on data_quality_scorecard to give a fleet-wide view' and explains that staleness/gap budgets are configurable per tag to handle different feed types. However, it does not explicitly state when NOT to use this tool or name alternative tools for single-endpoint analysis, so it falls short of a 5.

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