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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds the output range (0..1) and clarifies it's a semantic-relatedness score, which is not in annotations. No contradictions. However, it does not detail the model or knowledge base used.

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?

Single sentence, no wasted words. The key details (what it does, output range) are front-loaded. Highly concise.

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?

Output schema exists, so return value details are not needed. The description covers the core functionality. It could mention the underlying data source or default language handling, but the schema examples and parameter descriptions provide sufficient context. Missing use case guidance slightly lowers completeness.

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 coverage is 100% with descriptions for each parameter. The description does not add new meaning beyond the schema (e.g., 'node1' and 'node2' are already described). 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?

Clear and specific: 'Semantic-relatedness score (0..1) between two terms.' The verb 'compute' is implied, and 'score (0..1)' defines the output. It distinguishes from sibling tools like compare_entities or entity_profile by focusing on a numeric relatedness score.

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?

No guidance on when to use this tool vs alternatives. Lacks mention of use cases or when not to use. Sibling tools include other semantic/lookup tools, but the description does not differentiate.

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