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

Annotations already mark this as read-only and non-destructive, and the description adds matching behavior beyond that: case-insensitive matching, matching anywhere in the name, and returning exact entity names. This gives the agent useful expectations without contradicting the annotations.

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 carry the purpose, use case, example, and routing rule with no filler. The main behavior is front-loaded before the alternative guidance.

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?

The tool is simple, has full schema coverage, annotations, and an output schema, so the description does not need to explain return formats. It covers purpose, usage timing, matching semantics, and alternatives, making it complete for an agent to invoke correctly.

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?

The input schema already documents both parameters with 100% coverage, so the baseline is a 3. The description adds value by explaining how search_query is interpreted (case-insensitive, partial, matches anywhere) beyond the schema's 'Partial entity name' phrasing.

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 a specific verb and resource: search Nansen entity names by partial name and return exact matching names. It also distinguishes itself from general_search, which is for tokens or addresses, so an agent can tell sibling tools apart.

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?

It gives an explicit when-to-use rule: when a downstream tool needs an exact entity name but the user provided an approximate one, with a concrete example. It also states the alternative for tokens/addresses, making the routing decision unambiguous.

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