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

The annotations already declare the safety profile (readOnlyHint, idempotentHint, openWorldHint, destructiveHint=false), so the bar is lower. The description adds genuinely valuable context beyond the annotations: the determinism guarantee, the cost disclosure ('x402 price is $0.002 USD per call'), and the forbidden adjudication contexts. No contradiction with any annotation flag.

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?

Four short sentences, each carrying distinct information (function, intended use, exclusions, price), with the core function front-loaded. There is no filler, no restatement of the schema, and no redundancy — every sentence earns its place.

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 two-parameter computation whose annotations cover the safety profile, the description covers purpose, domain, exclusions, and cost well. The gaps are the absence of an output schema with no enumeration of which statistics are produced, no edge-case behavior, and no guidance for choosing among the four near-identical reliability_quality_benchmark_data_feed siblings.

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?

With 0% schema description coverage, the description must compensate, and it partially does: 'caller-supplied observations' maps to the observations parameter and 'explicitly named numeric fields' maps to metric_fields. However, it omits operational semantics such as what happens when a named field is missing from an observation, how non-numeric values are handled, and whether extraneous observation fields are ignored — leaving edge-case behavior to guesswork.

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 opens with a specific verb and object — 'computes deterministic descriptive benchmark statistics over caller-supplied observations and explicitly named numeric fields' — precisely identifying the operation and its inputs. The intended domain ('reliability quality / execution risk') helps place it among the many sibling feeds. It does not, however, differentiate it from the three other reliability_quality_benchmark_data_feed_* siblings that differ only by hash suffix.

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?

Clear guidance is provided in both directions: 'Intended for reliability quality / execution risk' states when it applies, and the categorical 'Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication' gives explicit exclusions. It stops short of naming a specific alternative tool among the many siblings, which keeps it from a 5.

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