UNDERSTANDING — the rubric: bands, facet weights, trend thresholds, so an agent can interpret any score. Moved behind the paywall with the ratings data on 2026-08-31. Priced B1 (single-entity synthesis) — $0.01 per call under pay-as-you-go; included in Understanding and Influence. See apis://prices.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional: one sentence on why you are asking. Never used to rank; read only if a result is wrong. |
Changes observed during successful MCP inspections.
Input schema / properties / context / descriptionPrevious value: -"Optional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable."New value: +"Optional: one sentence on why you are asking. Never used to rank; read only if a result is wrong."Input schema / properties / contextAdded value: +{
+ "description": "Optional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable.",
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), so the bar is lower. The description adds genuinely useful non-schema context: it is now paywalled, costs $0.01 per call under B1 pricing, is included in Understanding and Influence, and points to apis://prices. This is real behavioral/pricing disclosure beyond the annotations.
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 and front-loads what the rubric is and why an agent needs it, then appends the paywall/pricing facts. The 'UNDERSTANDING —' prefix is slightly odd formatting but overall there is little waste.
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?
With no output schema, the description still characterizes the rubric's contents (bands, facet weights, trend thresholds) and discloses cost/access constraints. For a zero-required-parameter getter this is reasonably complete, though it doesn't state the exact return structure.
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 the single optional 'context' parameter. The description says nothing additional about it. Baseline 3 is appropriate when the schema does the heavy lifting.
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 the specific resource and its contents: 'the rubric: bands, facet weights, trend thresholds, so an agent can interpret any score.' This tells an agent exactly what it retrieves. It does not, however, explicitly contrast itself against close siblings like get_provider_rating or get_rating_history.
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 by 'so an agent can interpret any score,' which suggests calling this to understand rating methodology, but there is no explicit when-to-use or when-not-to-use statement and no named alternative. The reader must infer the context.
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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