[Custom Enterprise Schema] Extracts CPT procedure codes, mandatory conservative therapy weeks, required preceding treatments, drug formulary tiers, and immediate approval red flags for health plan claims AI agents.
| 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 carries the full burden of behavioral disclosure. It describes outputs ('Extracts...') but does not state whether the tool is read-only, whether it requires authentication, whether it persists data, or what happens with invalid or unsupported URLs.
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 compact, front-loaded with the schema marker, and uses a single sentence to enumerate specific extracted entities. The '[Custom Enterprise Schema]' prefix is mildly redundant but not harmful.
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 lists key output categories, which is helpful given the absence of an output schema. However, it does not mention return format, URL requirements, error cases, or how structured the extracted data is, leaving some ambiguity for an agent preparing to invoke the 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 fully described by the schema ('Target URL to ingest and distill'), giving 100% schema coverage. The description adds context about the kind of information extracted but does not need to restate the parameter meaning.
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 a specific verb ('Extracts') and a specific resource scope ('health plan claims', 'CPT procedure codes', 'drug formulary tiers'), making it immediately distinguishable from sibling tools like pricing matrices, patent cliffs, and zoning compliance tools.
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 domain is obvious from the description and sibling names, so an agent can infer when to use this tool. However, there is no explicit guidance about when not to use it, what URL types are acceptable, or how it differs from the generic refinery_refine_custom_url tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.