sim_marketplace_search_tool

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the exact query behavior, including the Ollama LLM fallback, and specifies the return format (success, message, matches, total, llm_fallback_used). This goes beyond a simple description and tells an agent what will happen in edge cases.

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 well-structured with a one-sentence summary, filter list, return format block, and examples. It is slightly longer than strictly necessary, but every section adds useful information, especially the return schema and examples, making it easy for an agent to parse.

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 four-parameter search tool with an output schema and descriptive examples, the description covers core behavior, input options, fallback logic, and response shape. The only minor gap is not defining behavior when multiple filters are combined; exceptions, but this is not required for basic 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 input schema already covers all parameter meanings at 100% coverage, so the description does not need to repeat definitions. The description does add useful context for 'query' specifically (how exact-match failure triggers LLM fallback) and provides usage examples that illustrate how parameters map to calls, but these are enhancements rather than required clarification.

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 names a specific verb and resource ('Search the robot model marketplace for available models') and immediately states the supported filter dimensions: keyword, tags, sim backend, or robot type. This clearly differentiates it from siblings like sim_marketplace_info_tool and gazebo_models by emphasizing search and filtering rather than information-only or model-provider roles.

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 gives clear context for using the tool: search and filter the model marketplace, with example calls showing keyword, tag, and sim_backend filters. However, it does not explicitly state when NOT to use it or mention alternatives like sim_marketplace_info_tool, so it falls just short of strong routing guidance.

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