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

With no annotations, the description carries the full burden of behavioral disclosure. It states what data is extracted but does not disclose whether the operation is read-only, whether authentication is needed, how failures or non-extractable content are handled, or how results are returned.

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 compact sentence with a concrete itemized list, making the key information quick to parse. The '[Custom Enterprise Schema]' prefix adds little value, but the rest is lean and front-loaded.

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?

For a one-parameter extraction tool, the description names the input and the main categories of output, enough for a basic call. However, with no output schema, no annotations, and no sibling differentiation, it leaves gaps around result shape, processing behavior, and selection among similar refinery tools.

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 fully documents the only parameter, url, as 'Target URL to ingest and distill' (100% schema coverage), so the baseline is 3. The description adds domain context but no additional URL format, constraints, or usage semantics beyond what the schema already provides.

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 names a specific verb ('Extracts') and a precise list of outputs: active chemical ingredients, FDA 510(k)/NDA approvals, clinical trial phases, black-box warnings, and patent exclusivity expiration dates. It clearly identifies the tool's domain and purpose, though it does not explicitly differentiate it from overlapping siblings such as refinery_custom_clinical_trials or refinery_regulatory_compliance.

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?

There is no guidance on when to choose this tool versus its siblings or when it is not appropriate. The agent is left to infer applicability from the data categories and the tool name rather than being given explicit conditions or exclusions.

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