MCP Math Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool checks for infinity but does not describe the return value (e.g., boolean true/false), error handling (e.g., for non-numeric inputs), or performance considerations. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 extremely concise and front-loaded, with a clear purpose statement followed by domain/category tags. Every sentence earns its place, and there is no redundant or verbose language. It efficiently communicates the essential information without unnecessary elaboration.

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 low complexity (single parameter, no output schema, no annotations), the description is minimally complete. It states what the tool does but lacks details on return values, error cases, or usage context. While adequate for a simple check, it could be more informative to fully guide an AI agent, especially without annotations or output schema.

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 does not explicitly mention parameters, but with only one parameter ('x') and 0% schema description coverage, it implies the input is 'a number'. This adds minimal semantic context beyond the schema's type definition. Since the parameter count is low (1) and the purpose is straightforward, the description adequately compensates for the lack of schema details, though it could specify input constraints (e.g., accepts integers, floats).

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 explicitly states the tool's purpose as 'Check if a number is infinite (positive or negative infinity)', which is a specific verb ('Check') and resource ('a number'), clearly distinguishing it from siblings like 'is_finite' or 'is_nan'. It also includes domain/category context ('Domain: arithmetic, Category: comparison'), which further clarifies its role.

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 no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'is_finite' or 'is_nan', nor does it specify scenarios where this check is appropriate (e.g., after division by zero or handling special numeric values). Without such context, users must infer usage from the purpose alone.

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