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

With no annotations, the description carries the full behavioral burden. It usefully discloses billing ('Billing per call: Credits: metered (~0 avg)'), which is real added context, but omits any statement about output vector format/dimensions, model-dependent behavior, or error handling.

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?

Three short clauses, front-loaded with the core action. The 'Group: Embeddings' fragment is mild metadata padding but costs almost nothing.

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 model-inference call with no output schema and no annotations, the description should say something about the returned vector (shape, dimensions, usage tokens). It leaves the agent unable to predict the response shape.

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?

Schema coverage is 100% and the single body parameter already carries an inline JSON example, so the schema does the heavy lifting. The description adds no additional parameter meaning beyond that, which matches the baseline 3 for high coverage.

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?

States a specific verb and resource: creates an embedding vector from the input text. It is clearly distinct from siblings like get_models, post_audio_speech, and post_moderations, though it never explicitly names or contrasts 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?

No when-to-use guidance, prerequisites, or alternatives are given. The only usage-adjacent content is the 'Group: Embeddings' tag, which is categorization metadata rather than routing guidance.

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