insurer_increasesAn insurer's rate record across all its VHIS plans since 2021, by year, alongside the market table with every insurer ranked from lowest average annual change to highest.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | Brand name, e.g. Bupa, AIA, AXA; omit for the market table |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes that this is a safe read operation. The description adds useful context about the time range (since 2021), the yearly grouping, and the market-table ranking order, but it does not disclose anything about failure modes, pagination, or data freshness. This is acceptable for a read-only tool but does not go beyond what annotations already cover.
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 dense sentence with no filler or repetition. It packs several important details—time range, yearly granularity, insurer scope, and ranking order—into a compact form. It is not as crisp as a short imperative, but it is efficiently written.
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 simple read-only tool with one optional parameter and an output schema, the description provides enough information to understand what data will be returned: the insurer's year-by-year record and the ranked market table. The main missing piece is when to use this tool versus related siblings, but that gap is better captured under usage guidelines.
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 fully documents the only parameter, brand, with examples and the omit-for-market-table behavior, so schema coverage is 100%. The tool description adds no meaningful parameter detail beyond what the schema already provides, so the baseline of 3 applies.
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 clearly identifies the resource: an insurer's yearly rate record across VHIS plans since 2021, plus a ranked market table. It is specific enough to distinguish the general subject matter, though it lacks an explicit verb such as 'returns' or 'lists' and does not directly contrast against close siblings like premium_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?
The description offers no explicit guidance about when to choose this tool over related alternatives. The only usage hint, 'omit for the market table,' appears in the parameter schema rather than the tool description. An agent must infer when this tool is the right choice compared to premium_history or other rate-related tools.
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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