set_model_vibe

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

No annotations are present, so the description must carry the full burden. It states the action (set vibe summary and extra anchors) but does not disclose side effects (e.g., whether it overwrites or appends), required permissions, or failure behavior. The meta instruction about who calls it is not behavioral transparency on the tool's operation.

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 relatively well-structured with a clear opening sentence and bullet points for arguments. However, the paragraph about the LLM being the NLP extraction tier adds context but is not essential for tool invocation, making it slightly less concise than ideal.

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?

The description covers parameter semantics and usage context adequately. However, it lacks information about return values (though an output schema exists) and error conditions. For a tool with three parameters and simple behavior, the description is mostly complete but could improve with more behavioral details.

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 parameter details in the description add significant value. For model_id, it provides a format example; for vibe_summary, it specifies 'one sentence capturing the model's distinctive feel'; for extra_anchors, it gives an example list. This goes beyond the schema's mere type definitions.

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: 'Set the vibe summary and optional extra anchors for a model.' It also explains when it is used, distinguishing it from sibling tools that deal with searching, comparing, or indexing models.

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 explicitly says 'Called by the LLM after reading a model card or understanding a model's characteristics' and advises to 'delegate vibes extraction to the calling model rather than building NLP in Python.' This provides clear context on when to use the tool and when not to implement alternative logic, though it does not directly compare to sibling tools.

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