list_taxonomy

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

With no annotations provided, the description carries the full burden. It states 'Takes no parameters and has no side effects,' which is sufficient behavioral disclosure for a read-only, zero-param tool. No contradictions.

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?

Two sentences, no wasted words. First sentence states purpose and return content; second gives usage guidance. Efficiently front-loads key information.

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 simple tool with no parameters and no output schema, the description is complete: it explains what is returned, why to use it, and that it's side-effect free. It also ties to sibling tools, providing sufficient context.

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?

Since there are zero parameters and schema coverage is 100%, baseline is 4. The description adds value by detailing what the return contains (lists of strings for each category), beyond the empty schema.

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 returns 'every accepted enum value for the AI BVF taxonomy' listing specific categories (industries, functions, ai_tier levels, readiness levels). It also names the sibling tools that accept these values, distinguishing it from them.

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?

It explicitly advises to call this tool first when unsure about valid strings for certain sibling tools, providing clear usage context. While it doesn't state when not to use it, the instruction to call it when uncertain implies it's unnecessary when values are known.

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