entity_lookup

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

No annotations are provided, so the description carries full burden. It mentions return format but not side effects, error handling (e.g., entity not found), authentication, or rate limits. The example gives some idea, but transparency is moderate.

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 well-structured with clear sections: purpose, entity types, parameter details, returns, and example. Every sentence earns its place; no redundancy. It is front-loaded with the main purpose.

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 tool's complexity (3 params, no enums, output schema exists), the description is fairly complete. It covers parameters, provides an example, and mentions return data. Could include more on output schema constraints or edge cases, but not essential.

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 0%, so description must compensate. It explains each parameter with examples: entity_type (list of types), name (examples), properties (default behavior and example). This adds significant meaning beyond the schema.

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 looks up real-world entity data from Wolfram's curated knowledge base, with a specific verb 'Look up' and resource 'real-world entity data'. It lists entity types and provides an example, effectively distinguishing from siblings like wolfram_alpha which is more general.

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 does not explicitly state when to use this tool vs alternatives like wolfram_alpha or search data repository. It provides an example but no guidance on selection criteria, leaving the agent to infer usage context.

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