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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds substantial behavioral context: 81 legal-form spellings across 21 countries, word-order insensitivity, generic-word down-weighting, obfuscation flagging, and a distinctiveness caveat. This is exactly the kind of non-obvious behavior an agent needs to interpret results correctly.

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 purpose is front-loaded in the first sentence, and every subsequent sentence contributes a distinct behavioral fact or caution. There is no filler or redundant restating of the title or schema. The length is appropriate given the tool's complexity and the absence of an output schema.

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?

With no output schema, the description compensates well by stating the 0–1 score range, the reasoning breakdown, the obfuscation field, and the distinctiveness guidance. Minor gaps remain: the exact output structure is not fully specified, and type-dependent behavior for 'person' vs 'company' is not discussed beyond the schema default.

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 the baseline is 3. The description adds meaningful context by clarifying that a and b are names to compare and giving concrete examples of equivalence (e.g., 'Pty. Ltd.' = 'Proprietary Limited') and normalization behavior (word-order invariance). This goes beyond the schema's minimal 'First name.' and 'Second name.' descriptions.

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 opens with a specific action and outcome: 'Score whether two names are the same entity, 0 to 1.' This clearly identifies the tool's core purpose. It does not explicitly contrast with sibling tools like match_entity_name or normalise_entity_name, but the score-range and reasoning detail make it reasonably distinguishable.

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 the use case: call this when you need a same-entity similarity score between two names. It also adds an interpretive caveat about low-distinctiveness names. However, it never states when to prefer this over the sibling entity tools or mentions any exclusion conditions.

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