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

Annotations already establish readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds genuine context beyond that: 'No model call' (no generation side effect), that a cost quote is returned, and that missing or incomparable evidence is surfaced explicitly rather than silently omitted.

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?

Three short, front-loaded sentences with no filler; the read scope leads and the two behavioral caveats follow. It is dense and slightly cryptic, but every sentence carries information.

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?

An output schema exists, so return values need not be explained, and annotations cover safety. But for a tool with 7 required parameters at 0% schema coverage, the description's thin parameter hints leave an agent under-informed about how to populate the call correctly.

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 0% across 7 required parameters, so the description carries the full burden. It only loosely gestures at seriesId ('Series feedback') and from/to ('bounded'), and says nothing about goal, language, maxVariations, or workspaceId, leaving most parameters semantically undocumented.

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 gives a concrete verb ('Read') and enumerates the evidence types (Series feedback, metrics, clicks, self-reported outcomes) plus a cost quote, which roughly reconciles the cryptic name 'learning_quote' with the title. However, it never distinguishes itself from the sibling learning_get, and the domain jargon ('owner direction', 'Series feedback') leaves the actual purpose somewhat opaque.

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?

There is no statement of when to use this tool versus learning_get, learning_control, or learning_start, nor any prerequisite or exclusion. The only hint is 'No model call', which implies a read-only inspection but does not route the agent between the four learning_* tools.

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