lookup_part

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

With no annotations, the description carries full behavioral burden and thoroughly discloses matching logic: exact, then punctuation-insensitive, then Fortinet aliases. It also states 'Returns found:false rather than guessing', adding critical transparency about failure 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 tightly structured sentences cover purpose, return fields, and matching behavior. No filler or repetition; every sentence adds value.

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?

Despite lacking an output schema, the description covers inputs, matching logic, return data, and not-found behavior. It is complete for a single-item lookup tool with simple parameters and no annotations.

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?

Input schema has 100% coverage for both parameters, providing names and examples. The description adds meaningful semantics for part_number by explaining matching precedence and alias handling, which goes beyond the schema's simple format description.

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 'Look up one hardware part number in the EOSL.ai database' with a specific verb and resource, plus enumerates the return fields. It distinguishes itself from siblings like bulk_check by emphasizing singular lookup and from search_models by targeting exact part numbers.

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 phrase 'one hardware part number' establishes a clear single-item use case, contrasting with bulk_check. While it does not explicitly name alternatives or exclusions, the context is sufficiently clear for an agent to decide when to use it.

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