[Custom Enterprise Schema] Extracts city zoning classifications, short-term rental permits, mandatory inspection checklists, and penalty fine structures.
| 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?
With no annotations, the description carries the full burden. 'Extracts' communicates a read-only operation and lists expected output subjects, which is useful. However, it does not disclose URL requirements, access limitations, result formatting, pagination, or failure behavior, so transparency is partial.
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 definition is one efficient sentence with no filler. It front-loads the extraction purpose and lists concrete outputs, earning its place without redundancy.
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 extractor with no output schema, the description is reasonably complete: it names the input and the output categories. It stops short of full completeness by not specifying what constitutes a valid target URL or how results are returned, but those are minor gaps for this simple 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 input schema already documents the single url parameter as 'Target URL to ingest and distill', so the description adds no extra parameter-level meaning beyond the tool's broader purpose. Since schema description coverage is 100%, a baseline of 3 is appropriate.
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 extraction verb and enumerates specific outputs: city zoning classifications, short-term rental permits, inspection checklists, and penalty fine structures. This makes the tool's purpose concrete and distinguishes it from generic sibling tools such as 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?
No guidance is given about when to choose this tool over closely related siblings like refinery_custom_real_estate_zoning or refinery_regulatory_compliance. The description implies a use case but does not state prerequisites, exclusions, or alternatives.
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.