suggest_field_values

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'gets' suggestions (implying read-only) and that they're 'based on existing data,' but doesn't cover important aspects like rate limits, authentication requirements, response format, or whether it performs any data modification. For a tool with 3 required parameters and no annotation coverage, this is insufficient.

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 a single, efficient sentence that clearly communicates the core functionality. It's appropriately sized for this tool's complexity and front-loads the essential information without unnecessary elaboration. Every word serves a purpose in explaining what the tool does.

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 moderate complexity (3 required parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks important contextual information about when to use it, how results are returned, and behavioral constraints. Without annotations or output schema, the description should provide more complete guidance for proper tool invocation.

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 description adds some context about parameter semantics by mentioning example fields (brand, color, size, tags) and providing an example value match ('vin' to find 'Vintage Heritage'). However, with 100% schema description coverage, the schema already documents all parameters thoroughly. The description provides marginal additional value but doesn't significantly enhance understanding beyond what the schema provides.

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: 'Get auto-complete suggestions for a specific field... based on existing data.' It specifies the action (get suggestions), target (field values), and data source (existing data). However, it doesn't explicitly differentiate from the sibling 'search_suggest' tool, which appears to serve a similar suggestion function.

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 no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'search_suggest' tool or explain what distinguishes them. There's no context about prerequisites, limitations, or appropriate use cases beyond the basic functionality stated.

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