Meta dataset quality

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), and the description reinforces it with the concrete claim 'nothing is sent to the dataset', which clarifies this is a non-mutating diagnostic read. Because no output schema exists, the enumeration of returned metrics (EMQ composite, match key feedback, coverage, dedup, freshness, ACR) adds genuine behavioral value beyond the structured fields.

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?

Two tight sentences with the metric inventory front-loaded and the read-only assurance placed at the end where it belongs. The metric list is dense but every element is informative, with no filler.

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 zero-required-parameter, read-only diagnostic with no output schema, the description supplies the essential missing piece by naming the metrics in the response. It stops short of explaining interpretation or the reporting window, but an agent has enough to invoke it 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 100%: both pixel_id and client_id are documented in the schema, including the empty-value fallback behavior. The description adds no parameter-level detail (e.g., which id takes precedence or what time window is reported), 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 names the specific resource (Conversions API / pixel dataset) and enumerates the exact metrics returned (event match quality, event coverage, dedup feedback, freshness, ACR), which tells an agent precisely what the tool produces. It does not, however, distinguish itself from the close sibling meta_pixel_health, so sibling differentiation is missing.

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 reach for this tool versus alternatives such as meta_pixel_health, meta_catalog_diagnostics, or meta_insights. Usage must be inferred entirely from the metric list, and no prerequisites or exclusions are given.

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