Zoom MCP Server

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 effectively describes key behavioral traits: the tool's efficiency (single API call without location_query vs. multiple with it), smart location resolution, and performance implications. However, it lacks details on error handling, rate limits, or authentication needs, which are relevant for a tool with API calls.

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, starting with the core purpose. Each sentence adds value: the first states the purpose, the next two provide important usage guidelines with efficiency tips, and the examples reinforce the guidance. There is no wasted text, and the structure is clear and logical.

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 moderate complexity (1 parameter, no output schema, no annotations), the description is largely complete. It covers purpose, usage, parameter semantics, and behavioral aspects like efficiency. However, it lacks details on output format (what data is returned) and potential errors, which would enhance completeness for an API-based tool.

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 input schema has 0% description coverage, so the description must fully compensate. It adds significant meaning beyond the schema by explaining the semantics of location_query: when to use it (for specific location filtering), when to omit it (for company-wide queries), and examples (e.g., 'SF1', 'DEN1'). This clarifies the parameter's purpose and usage context effectively.

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: 'Get Zoom rooms with optional location filtering.' It specifies the verb ('Get'), resource ('Zoom rooms'), and scope ('with optional location filtering'), distinguishing it from siblings like get_room_details (specific room details) and get_zoom_sites (sites rather than rooms).

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 explicit guidance on when to use this tool versus alternatives, including detailed scenarios. It specifies when to omit location_query (for company-wide queries) and when to use it (for location-specific filtering), and mentions efficiency trade-offs (single vs. multiple API calls), which helps differentiate from siblings like resolve_location for location resolution.

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