[Custom Enterprise Schema] Extracts clinical trial phases, target molecular biomarkers, primary endpoints, and enrollment criteria.
| 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 full behavioral burden. It discloses that the tool extracts certain data from a URL, but it does not state whether the operation is read-only, whether it fetches the URL itself, what the output format is, whether authentication is needed, or how failures are handled. The phrase 'ingest and distill' in the schema hints at processing but does not clarify the tool's behavior.
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 concise sentence with no filler. It front-loads the core action and enumerates the extracted data types efficiently, which is appropriate for a one-parameter tool.
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 is adequate for a simple URL-to-extraction tool: it names the input and the extracted fields. However, with no output schema and no annotations, it lacks details about return structure, failure behavior, and how to choose between this and other refinery custom-schema tools, leaving meaningful gaps for an agent.
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 schema already covers the single parameter with 100% coverage: 'Target URL to ingest and distill.' The description adds context about what kinds of information will be extracted from the URL, which helps an agent understand what URL content is relevant, but it does not add constraints, format, or syntax details beyond the schema.
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 specific verb ('Extracts') and a concrete resource ('clinical trial phases, target molecular biomarkers, primary endpoints, and enrollment criteria'), making the tool's function immediately clear. It also distinguishes itself from sibling tools like refinery_custom_biopharma_fda_patent_cliffs and refinery_custom_health_insurance_clinical_policy by targeting clinical trials specifically.
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?
Usage is implied: the tool should be used when a URL needs to be turned into structured clinical-trial information. However, the description gives no explicit guidance on when to prefer this tool over siblings such as refinery_refine_custom_url or refinery_semantic_search, and no exclusion criteria are stated.
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.