suggest_meeting_point

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the calculation logic ('calculates a central meeting point based on the locations'), then venue recommendation, and lists return values (center coordinates, list of venues, total count). It does not mention edge cases or external service dependencies, but it provides a solid high-level behavioral overview without contradicting any 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?

The description is well-structured with clear sections: an introductory summary, a use-case sentence, an 'Args' block, and a 'Returns' block. It is concise and front-loaded with the core purpose, with every sentence contributing useful information. No redundancy or filler.

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 has no output schema, so the description correctly explains the return values (center point, suggested venues, total number). It also covers all parameters and the algorithm flow. Given the tool's moderate complexity and the absence of structured outputs, the description is sufficiently complete for an agent to select and invoke the tool 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?

Schema description coverage is 0%, so the description fully compensates. It explains that 'locations' is a list of dictionaries with latitude and longitude, provides a concrete example, and clarifies 'venue_type' with typical options like 'cafe', 'restaurant'. This adds significant meaning beyond the bare schema, making it easy for an agent to construct valid inputs.

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: 'Find the optimal meeting place for multiple people coming from different locations.' It uses a specific verb ('find') and resource ('meeting place'), and explains the two-step process (calculate central point, then recommend venues). This distinguishes it from sibling tools like find_nearby_places or geocode_address, which handle single locations or simple searches.

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 clear usage context: 'Ideal for planning social gatherings, business meetings, or any situation where multiple people need to converge from different starting points.' This implies when to use the tool, but it does not explicitly mention alternatives or when not to use it. Since the siblings are not referenced, it misses explicit exclusions, so a 4 is appropriate.

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