search_models

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

With no annotations, description discloses use of source adapters, merging for 'all', and filter applicability. It is clear about behavior but could mention pagination or error handling for completeness.

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?

Description is front-loaded with a one-line summary, followed by a brief explanation of adapter behavior, and then a structured args list. Every sentence adds value and there is no wasted text.

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 presence of an output schema, return values need not be explained. The description covers core functionality, parameters, and special case ('all' merges). Could mention prerequisites like indexing, but overall adequate.

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 description coverage is 0%, so description bears full burden. It explains all 5 parameters (query, source, limit, task, author) with details on defaults and source-specific applicability, adding significant meaning beyond the schema.

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?

Description clearly states 'Search models across sources (huggingface, ollama, or all)' and explains merging behavior for 'all'. This distinguishes it from sibling tools like hf_search_models which are Hugging Face-specific, giving a specific verb+resource purpose.

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?

Description implies when to use different sources (specific vs all) but does not explicitly exclude alternatives or state when not to use this tool. Sibling differentiation is implied but not explicit, so a score of 4 is appropriate.

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