[Custom Enterprise Schema] Extracts normalized monthly/annual costs, seat limits, included token/bandwidth quotas, and hidden overage terms across SaaS vendors.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Target URL to ingest and distill |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the transparency burden. It explains that the tool extracts normalized costs, seat limits, quotas, and overage terms, which conveys a read-only extraction behavior. However, it does not mention failure modes, access constraints, rate limits, or how the extracted data is 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, tightly worded sentence with no filler. The '[Custom Enterprise Schema]' marker is front-loaded, and every phrase contributes useful information.
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 core input and output dimensions are named, which is adequate. But with no output schema and no annotations, there is still a gap around expected response shape, limitations, and how this tool relates to the sibling pricing-matrix 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 only parameter, url, is already well documented by the input schema ('Target URL to ingest and distill'), and schema coverage is 100%. The description adds domain context but does not materially expand the meaning of the url parameter itself.
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 states a clear verb ('Extracts') and a concrete resource: normalized pricing, quota, and overage data for SaaS vendors. It is specific enough to convey the tool's purpose, but it does not explicitly distinguish itself from the near sibling refinery_b2b_pricing_matrix beyond the '[Custom Enterprise Schema]' tag and 'SaaS' qualifier.
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 phrase 'across SaaS vendors' implies when this tool is relevant, but there is no explicit 'use this when' guidance, no exclusions, and no direction about choosing between this and refinery_b2b_pricing_matrix. An agent must infer the intended use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Several tools form near-overlapping pairs: b2b_pricing_matrix vs custom_b2b_saas_pricing_matrix, dev_breaking_changes vs custom_dev_sdk_breaking_changes, and municipal/real-estate zoning vs regulatory_compliance. While individual descriptions differ, an agent would often have to guess which variant applies.
Names share a refinery_ prefix and use snake_case, making them mostly predictable and readable. The custom_ qualifier is used inconsistently—custom schema vs custom URL—and refinery_refine_custom_url/semantic_search break the otherwise noun-object pattern, but these are minor deviations.
13 tools is reasonable for a data-refinery platform covering multiple vertical schemas. The redundancy between base and custom_ variants and overlapping compliance tools makes it slightly heavier than necessary, but not excessive.
The surface covers refining arbitrary URLs, semantic search, and many domain-specific extraction schemas. Missing are database/schema management, document-level retrieval/update/delete, and clear parity between base and custom variants, so agents may hit dead ends when managing or verifying refined data.