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 adds value by stating that the parameters include 'numeric values, units, and individual confidence levels,' giving insight into the data's nature. It also explains the filtering capabilities. However, it does not mention permissions, side effects, or pagination, but for a read-only retrieval tool, the provided details are reasonably transparent.

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 well-structured and concise. It leads with the core purpose, follows with the primary use case, provides a helpful example, and ends with a straightforward Args list. Every sentence contributes to understanding, and there is no redundancy or filler.

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?

The description covers the tool's purpose, usage, and parameters. Since an output schema exists, it need not explain return values. It does not explicitly mention default behavior when no filters are applied, but the schema's default null values imply 'get all.' The example adds practical context, making the description fairly complete for a simple retrieval 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?

Although the schema has 0% description coverage, the description's Args section thoroughly explains both parameters: 'domain: Filter by domain slug (optional)' and 'parameter_name: Filter by parameter name substring (optional).' This fully compensates for the missing schema descriptions and adds clear meaning beyond the parameter names.

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 function: 'Get quantitative parameters from knowledge entries.' It specifies the resource (knowledge entries) and the type of data (quantitative parameters), distinguishing it from sibling tools like get_cross_references or get_domain_stats. The addition of the cross-domain consistency checking use case further clarifies its purpose.

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 explicitly says 'Use this for cross-domain consistency checking,' which provides clear context for when to invoke this tool. It does not explicitly name alternatives or conditions to avoid using it, but the use case is specific enough to guide an agent. The example illustrates a concrete scenario, further reinforcing the intended usage.

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