yelp_agent

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

With no annotations provided, the description carries the full burden and adds significant behavioral context. It discloses that the tool returns both natural language responses and structured data, maintains conversation context, requires including Yelp URLs in recommendations, and handles capabilities like reservation booking exclusively through Yelp Reservations. However, it doesn't mention rate limits, authentication needs, or error handling.

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 appropriately sized and front-loaded with key information, though it includes a lengthy 'Capabilities include' list that could be more concise. The CRITICAL note and examples are useful but add bulk. Most sentences earn their place by clarifying functionality or usage.

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 (agent with multi-turn interactions, diverse capabilities) and no annotations or output schema, the description does a good job covering purpose, behavior, and parameters. It explains return types (natural language and structured data) and provides examples. However, it doesn't detail error cases or the exact structure of returned data.

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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains that 'natural_language_query' is for 'any business-related request,' 'search_latitude/longitude' are for 'precise location-based searches,' and 'chat_id' is for 'follow-up questions and conversational context.' This fully compensates for the schema's lack of 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 clearly states the tool's purpose as an 'intelligent Yelp business agent' that 'handles any natural language request about local businesses' through conversational interaction with Yelp's data. It specifies the verb ('handles'), resource ('local businesses'), and distinguishes it as an agent-to-agent communication tool with multi-turn capabilities.

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 provides implied usage through examples and the statement 'handles any natural language request about local businesses,' but lacks explicit guidance on when to use this tool versus alternatives. Since there are no sibling tools mentioned, the absence of comparative guidance is less critical, but it doesn't specify prerequisites or exclusions beyond the examples.

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