Suggest meeting point

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

No annotations are provided, so the description carries the full burden. It reveals that the tool 'computes the central point' and 'recommends nearby venues', which are read-only behaviors. However, it doesn't disclose edge cases like no venues found or how 'optimal' is determined, leaving some ambiguity.

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 a single, well-structured sentence with a colon separating the primary action from the supporting detail. Every word earns its place; no waste.

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 there is no output schema, the description reasonably explains what the tool returns ('recommends nearby venues') and the core algorithm. It lacks details on return format or failure handling, but for its simplicity and clear schema coverage, it is largely complete.

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 100% for both parameters, including descriptions for locations and venue_type with examples. The description adds no additional parameter-level semantics, so the baseline of 3 is appropriate.

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' and distinguishes it from siblings like find_nearby_places by emphasizing the central point computation for multiple locations.

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 context: use this when multiple people come from different locations and you need a meeting point with a venue type. It does not explicitly name alternative tools or exclusion criteria, but the scenario is well-defined.

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