High precision statistics

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 fails to mention that the tool is read-only (non-destructive), whether there are rate limits, or how inputs like string values are handled. The description only lists operations, lacking essential behavioral context.

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, efficient sentence listing all operations in a readable format. It front-loads the key information without extraneous words. However, it could be slightly restructured to separate operations from usage notes, but overall it is appropriately concise.

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 has 6 parameters (2 required) and no output schema, the description is incomplete. It omits critical details: parameter dependencies (percentile requires the 'percentile' param), output format, and error handling (e.g., empty array). The 33% schema coverage exacerbates the gaps, leaving the agent without sufficient context for correct invocation.

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 only 33% (only sample and percentile have descriptions). The tool description does not add meaning to other parameters (format, precision, values). For example, it doesn't explain that 'values' can include string numbers or that 'precision' controls significant digits. The low coverage and lack of explanation hinder correct invocation.

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 computes common statistical measures (count, sum, mean, etc.), which distinguishes it from sibling tools like arithmetic (basic operations) and calculus (differentiation/integration). The verb 'compute' and enumeration of specific resources make the purpose unambiguous.

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 lists operations but provides no guidance on when to use this tool versus alternatives (e.g., arithmetic for simple sums). It does not specify prerequisites, typical use cases, or when to avoid using the tool. Usage is implied but not explicitly stated.

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