Lookup verified, structured pricing tiers, feature checklists, token costs, and overage rates for B2B SaaS/AI tools.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter by category (e.g., 'Observability', 'AI Gateway', 'Vector DB') | |
| companyOrProduct | No | Name of product or company (e.g. 'DataDog', 'OpenAI', 'Supabase') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden of behavioral disclosure. It indicates this is a read-only lookup, but it does not disclose what happens when no match is found, whether coverage is limited to 'verified' tools only, or how current the data is. For a simple lookup tool, this is a moderate gap.
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, front-loaded sentence with no filler. Every word contributes to conveying the tool's purpose and content, making it highly efficient.
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 the types of data returned (pricing tiers, feature checklists, token costs, overage rates), which is helpful given there is no output schema. However, it does not explain output format, limitations, or how this tool differs from the closely named custom pricing matrix sibling, leaving the agent with some ambiguity about scope and alternatives.
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%, so the schema already fully documents both 'category' and 'companyOrProduct'. The description adds no new parameter-level detail, but does not need to; it stays at the baseline 3.
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 uses a specific verb ('Lookup') and resource ('verified, structured pricing tiers... for B2B SaaS/AI tools'), clearly stating what the tool does and what kind of data it returns. However, it does not explicitly distinguish itself from the similarly named sibling 'refinery_custom_b2b_saas_pricing_matrix', so the uniqueness is somewhat ambiguous without further inspection.
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 description gives a general sense of when to use the tool (when you need B2B SaaS/AI pricing data), but it provides no explicit guidance on when to use this tool versus the custom pricing matrix sibling, nor does it mention any exclusions or alternatives. There is no 'when-not-to-use' information.
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.