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

No annotations are provided, so the description carries the full burden. It discloses the return behavior ('Returns ranked candidates with similarity scores') and the fuzzy-matching behavior, which is useful context. However, it does not mention what happens when no candidates meet the threshold, which is a minor gap.

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 concise, with three sentences: purpose, usage guidance, and return info plus example. It is front-loaded and every sentence contributes value. The example is helpful without being 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?

Given the low complexity (two parameters, no output schema), the description covers the core aspects: what it does, when to use it, and what it returns. It lacks details about edge cases (e.g., empty results), but the provided information is sufficient for a simple search 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% with both parameters documented. The description adds an example call but does not provide additional semantic details beyond the schema. The baseline of 3 applies, as the schema already explains the parameters well.

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 for a data model by approximate or misspelled name using fuzzy matching.' It specifies the verb (search), resource (data model), and method (fuzzy matching), and distinguishes it from siblings by positioning it as the recovery step when get_data_model fails.

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?

Explicit usage guidance is provided: 'Use this as the recovery step whenever get_data_model returns MODEL_NOT_FOUND.' This directly states when to use the tool, and the mention of 'finds the closest real model names' implies the alternative fallback use case.

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