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

No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses the key behavior: it is a paid call (10¢), returns a range and confidence score, and works per single GNAF. However, it doesn't clarify whether the call is read-only or if any side effects exist (likely read-only), or if there are rate limits or authentication specifics.

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 extremely concise, using two sentences to convey essential information: the tool's function, output types, and cost. It front-loads the core function and avoids any fluff.

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?

Given the tool's simplicity (single parameter), the description is largely complete. It explains the output structure (point estimate, range, confidence score) and cost, but lacks details on the exact meaning of the confidence score and any edge cases (e.g., invalid GNAF). The output schema is not provided in detail, so the description helps, but a few more specifics would make it fully complete.

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?

The schema has only one parameter with no description, giving 0% coverage. The description compensates by explaining the parameter's role: 'single GNAF' and that it determines the property to value. However, it doesn't specify the format or expected source of the GNAF (e.g., whether it's a string of digits, how to obtain it), which is a minor gap.

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 it is a Machine Learning AVM for a single GNAF, producing a point estimate, 80% range, and confidence score. It names the unique resource (GNAF) and distinguishes itself from valuation siblings by specifying the AVM method and output components.

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 implies usage for a single property valuation via GNAF ID, but does not explicitly state when to use this tool versus other valuation tools (e.g., properties_valuation_all, properties_valuation_agent_quoting). No alternatives are named, leaving routing decisions to the agent.

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