Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the behavioral burden. It discloses that the tool is paid, returns only corrections rather than the full dataset, and is backed by a monthly re-read against vendor pages. It doesn't mention pagination or exact return shape, but the core behavioral context is well covered.

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 three focused sentences with no filler. The paid status and core action are front-loaded, followed by useful context about freshness and cost comparison.

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 one-optional-parameter read tool with no output schema, the description covers purpose, filtering behavior, data freshness, cost, and how it compares to alternatives. It doesn't specify the exact return format, but that is a minor gap given the tool's simplicity.

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 schema already fully describes the only parameter ('Optional YYYY-MM-DD. Defaults to 30 days back'), so the description adds little semantic value for the parameter. With 100% schema description coverage, the 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 uses a specific verb ('Return') and a specific resource ('dated Agent Pricing Index corrections'), and immediately distinguishes itself from the full-dataset tools by saying 'what changed, not the full 47-row dataset.' There is no ambiguity about the tool's purpose.

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?

It gives a clear selection context—'when you only need to know what moved'—and contrasts itself with 'fde equivalent full-dataset calls' as cheaper. It doesn't name a specific sibling tool or state explicit when-not-to-use conditions, but the guidance is enough for an agent to choose correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.