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

With no annotations, the description carries the full burden and does so: it discloses auth requirements (Bearer API key), cost (2 credits, $0.0053–$0.02 per credit with a pricing link), the proxied endpoint and payload shape, a 16 KB size cap, caller-order preservation, and that unknown fields remain null.

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?

Front-loaded with the purpose, then verification details in a tight sequence. Every sentence carries information; the pricing URL parenthetical is the only slightly expendable element, but it supports the stated credit cost.

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?

No output schema exists, yet the description explains the return shape (sourced snapshot facts, offerings, evidence, caller order, null for unknowns) plus auth, cost, and payload limits. An agent has everything needed to invoke and interpret results correctly.

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%, so the parameter is already documented; the description adds the wire-level constraint ('{models: [...]} at most 16 KB') and repeats the 2–4 count and caller-order semantics. The byte cap is genuine added value beyond the schema, though most content duplicates structured fields.

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 ('What facts do 2 to 4 named models have?') and immediately delimits scope with 'doesn't choose a model and does not rank', separating it from siblings like decide, rank, and model_info. An agent can select it correctly without opening the schema.

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 negative framing ('doesn't choose a model and does not rank') gives clear when-not guidance against competing siblings, and the fact-fetching framing implies when to use it. It stops short of naming which sibling to use instead for ranking or selection decisions.

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