query_compatibility

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

With no annotations, the description carries the burden. It discloses that the tool is read-only (querying) and overlays two data sources, flagging conflicts. However, it doesn't discuss permissions, rate limits, or the exact conflict detection logic.

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 concise (3 short sentences) and front-loaded with the core purpose. The use of backticks and asterisks adds clarity for 'declared', 'observed', and 'conflicts'. No unnecessary words.

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 (combining two data sources with optional filtering), the description omits crucial details: how to filter by module/platform, output structure, conflict criteria, and relationship to sibling tools. An output schema exists but is not described.

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?

The input schema has 0% description coverage, and the description does not explain the purpose or expected values of the 'module' and 'platforms' parameters. A user cannot infer how to use these parameters from the description alone.

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 purpose: querying compatibility data by overlaying declared (netlab) and observed (lab) supports. It distinguishes itself from sibling tools like validate_in_lab by focusing on data comparison and conflict flagging.

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 explains the data sources (declared from netlab, observed from prior runs) but provides no guidance on when to use this tool versus alternatives like get_known_good or validate_in_lab. No exclusions or prerequisites are mentioned.

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