[Custom Enterprise Schema] Extracts deprecated functions, removed parameters, breaking signature shifts, and exact before/after code migration snippets for AI coding 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?
With no annotations, the description carries the full burden of behavioral disclosure. It clearly reveals the types of output the tool produces, which is useful, but it does not mention limitations, output format, or any side effects. This is adequate but leaves clear gaps for an agent reasoning about 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, tightly-scoped sentence that front-loads the key behavioral outcome and lists concrete output categories. There is no filler, repetition, or unnecessary qualification.
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 tool has only one parameter and no output schema, so the description's specification of extracted content types helps. However, it does not explain the response format, nor does it route the agent away from the similar generic breaking-changes sibling. It is workable but not complete on its own.
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?
Schema description coverage is 100%, and the single 'url' parameter is already described as 'Target URL to ingest and distill'. The tool description does not add parameter-level detail beyond that, so the baseline score 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 specific action — 'Extracts deprecated functions, removed parameters, breaking signature shifts, and exact before/after code migration snippets' — with a clear resource and target audience. It is detailed enough to distinguish from more generic breaking-change tools, though it does not explicitly contrast itself with sibling 'refinery_dev_breaking_changes'.
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 provided about when to use this tool versus the related 'refinery_dev_breaking_changes' or other custom schema tools. The description implies usage for AI coding agents needing migration help, but it never states exclusions, prerequisites, or when a sibling is preferable.
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.