get_office_meeting_rooms

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 explicitly warns that the data has not been updated since 2024 and may differ from current operations, which is a significant behavioral caveat. It also discloses the data scope (headquarters building) and the nature of the output (list of rooms with code, capacity, specs). It does not state whether the operation is read-only, but for a list endpoint this is obvious from context. The staleness warning and scope note provide solid transparency beyond what the schema conveys.

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 compact—four lines total—and front-loads the core purpose (room list lookup). It then provides the key data attributes, the range of rooms, and the staleness warning, ending with the parameter explanation. Every sentence contributes value; there is no redundancy or fluff. The structure flows logically from purpose to specifics to caveat to filter.

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 low complexity (one optional parameter, no nested objects, and an output schema available), the description covers all necessary context: the scope (headquarters), the number of results (11), the data fields provided, the range of room sizes, and the staleness caveat. The filter is explained with an example. Since an output schema exists, the description need not detail return formats. The description is complete for an agent to decide whether to call the tool and how to use its single parameter.

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 schema has zero description coverage for the min_capacity parameter, so the description must compensate. It does so effectively, explaining that min_capacity filters rooms to those with at least the specified capacity, and even gives an example (30 means 30 or more). This adds clear semantic meaning beyond the bare integer type and default value. While it could mention edge cases (e.g., behavior with 0), the explanation is sufficient for the single parameter.

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: it retrieves the list of meeting rooms in the headquarters building, provides 11 entries, and details the specific attributes (room code, capacity, specs). It is unambiguous and distinct from all sibling tools, which are unrelated domains like stations, freight, and trains. There is no ambiguity about what this tool does.

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 implies usage (for querying meeting room lists) and explains the one filter (min_capacity), but it does not state when to use this tool versus alternatives, or when not to use it. Since no sibling operates on meeting rooms, this is a minor gap, but the description lacks explicit context about the intended scenario (e.g., 'when a user needs meeting room capacity information'). The filter guidance is present, but broader usage context is implied rather than stated.

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