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

With no annotations, the description carries full behavioral disclosure. It explains the search modes (keyword, domain, partial model name), states that it returns 'a list of matching model names and their subjects', and includes an example call. It does not detail matching semantics (exact vs fuzzy), ordering, or pagination, but for a search tool this is acceptable and transparent enough.

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 only two sentences plus an example, with the primary purpose front-loaded. There is no redundancy or fluff, and the example is instructive without being overly verbose.

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 tool with 3 parameters, no annotations, and no output schema, the description covers purpose, usage context, search modes, and return structure. It does not mention the limit parameter (but the schema covers it) or result ordering, but overall the description is sufficient for an agent to correctly invoke the tool.

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 coverage is 100%: each parameter (query, domain, limit) already has a description. The description's example adds a concrete usage, but does not introduce new meaning beyond the schema. Since the schema does the heavy lifting, baseline 3 is appropriate.

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: 'Search the Smart Data Models (SDM) catalog by keyword, domain name, or partial model name.' It specifies a verb and resource, and the phrase 'discover which data models exist for a topic' clarifies the use case. The example further solidifies understanding and distinguishes it from sibling tools like list_models_by_domain or fuzzy_find_model by listing explicit search modes.

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 a clear usage trigger: 'Use this when the user wants to discover which data models exist for a topic (e.g., "parking", "weather", "energy meter")'. This tells the agent when to select this tool. However, it does not mention alternatives or exclusions, so it falls short of a 5.

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