triz_evals

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

No annotations are supplied, so the description carries the full burden. 'Return' implies a side-effect-free read, but it does not say whether results are static fixtures or computed, how large the payload is, whether there is pagination or filtering, or how failures/regressions are represented. For a tool with zero annotation coverage this is a meaningful gap.

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?

One sentence, front-loaded with the verb, with no filler or redundancy. It is lean but also thin; nothing is wasted, though little is conveyed.

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?

There is no output schema and no annotations, so the description is the only place the agent could learn what comes back. It gestures at 'regressions' and 'reasoning cases' without describing their structure, count, or format, which leaves the return contract undefined for a tool whose entire purpose is returning data.

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 tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The single sentence adds no parameter-level meaning, but none is needed.

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 verb 'Return' plus the resource 'TRIZ lookup regressions and cross-domain reasoning cases' identifies the tool as a retriever of evaluation fixtures, but 'regressions' is jargon that could mean test cases, known-failure records, or regression results. It is distinguishable from siblings like triz_matrix or triz_principle (which expose TRIZ content proper), but the reader must guess what an 'eval' actually is here.

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?

There is no statement of when an agent should call this versus the other 50 TRIZ tools, no mention of whether it is for validation, self-testing, or calibration, and no prerequisites or exclusions. The agent must infer usage entirely from the name.

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