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

Annotations already indicate readOnlyHint=true. The description adds significant behavioral context: use of vector embeddings, return fields (similarity scores, status, marketplace data), and a critical caveat about GRACE_PERIOD names not being registerable by others. No contradictions.

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?

Three sentences, each serving a purpose: purpose and method, output overview, and a behavioral note. No redundant or vague content. Well-structured and efficient.

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?

Despite no output schema, the description fully explains return values and adds context about the indexed dataset and registration restrictions. For a search tool with good annotations and schema, this is complete.

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%, so baseline is 3. The description does not add extra meaning beyond schema descriptions for the parameters themselves, but it does clarify output semantics (e.g., status types). This is adequate but not exceptional.

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 the tool finds ENS names semantically similar to a given name using vector embeddings, and specifies the indexed dataset size (3.5M). It distinguishes from sibling tools like search_ens_names (exact/prefix) by emphasizing semantic similarity.

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 implies usage when you need alternative names based on semantics, but does not explicitly state when to use vs alternatives. It provides clear context for its function, but lacks exclusions or when-not scenarios.

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