get_data_quality_grade

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

Annotations already supply readOnly/idempotent/destructive safety, so the description correctly uses its space for deeper semantics: the grade is computed on the latest reported fiscal year, is the worse of two gates, and notably that "a missing grade is not a bad grade" — a genuinely useful interpretive caveat. It also previews the payload (counts, direct vs derived split, GAAP/IFRS presence, freshness), though the output schema already covers return shape.

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 core purpose and the A+/D range are front-loaded, followed by the computation semantics and the missing-grade caveat. It is dense but every clause carries meaning; the trailing enumeration of payload fields is slightly list-like but still informative.

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 single-parameter read tool with a full output schema, the description covers purpose, computation, interpretation, and payload contents well. The one gap is that it never describes the entity_id parameter, which the schema also leaves undocumented.

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 sole parameter entity_id is never mentioned or explained in the description. While the name is somewhat self-evident given siblings like find_entity, the description does nothing to compensate for the missing schema documentation, leaving the agent to infer format and source.

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 states a precise verb+resource (compute/return a data-quality grade for an entity) and defines the output domain (A+ to D, or none). It distinguishes itself from siblings like get_validation_results by framing the grade around completeness and the two gates it uses, so an agent can tell it apart without opening other schemas.

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 when the user asks how reliable a company's data is" gives a clear triggering context. It stops short of naming alternatives (e.g., get_validation_results) or stating when-not to use it, so it is strong but not fully explicit.

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