get_taste_context

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns aggregated feedback and that all filters are optional (which implies default behavior when omitted). However, it doesn't describe important behavioral aspects like response format, pagination, rate limits, authentication requirements, or whether this is a read-only 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 efficiently structured with purpose first, usage context second, example third, and parameter details last. Every sentence adds value - no redundant or wasted words. The parameter explanations are clear and direct without unnecessary elaboration.

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 4-parameter tool with no annotations and no output schema, the description does well on purpose and parameters but lacks important behavioral context. It doesn't describe the return format, error conditions, or operational constraints. The example helps but doesn't fully compensate for the missing structural information about what the tool actually returns.

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 clear semantic explanations for all 4 parameters. Each parameter gets a specific explanation of what it filters for, with concrete examples ('reliability_engineer', 'lubricants', 'explain_prediction'). The description also clarifies that all filters are optional and what happens when omitted.

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 with specific verbs ('retrieve organizational taste', 'understand audience preferences') and distinguishes it from siblings by focusing on aggregated expert feedback about explanation quality. It explains what 'taste' means in this context (what experts think good explanations look like).

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 clear context for when to use this tool ('before presenting results' to 'understand audience preferences') and gives a concrete example of how the feedback can be applied. However, it doesn't explicitly contrast when to use this versus specific sibling tools like 'retrieve_business_context' or 'get_skill'.

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