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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely new context: no API key needed, stale readings are filtered by default, and each metric ships a verify_url on the network's own API that can be opened to independently check the figure. That verification path is not derivable from the annotations or schema.

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 sentences, front-loaded with the resource and payload contents, followed by two short behavioral constraints. The opening sentence is long and parenthetical, but every clause carries distinct information and nothing is redundant 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?

An output schema exists, so return values need not be spelled out, yet the description still previews the per-metric shape (value, unit, as_of, freshness, verify_url), which is useful. Combined with the coverage/exclusion notes and the no-key statement, an agent has what it needs; only the alternative-tool routing (get_metric_history, find_networks) is missing.

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%, so every parameter is already documented in the schema. The description restates the metric_filter substring behavior and the include_stale default without adding new semantics (e.g., what counts as 'stale', or how max_metrics interacts with filtering). Baseline 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 gives a specific verb and resource ('Returns the latest first-party measurements for one DePIN network') and enumerates the metric families covered (GPU/CPU capacity, storage, node/hotspot counts, on-chain totals). It also sets a scope boundary ('Market prices are not included'), which helps separate it from a price-oriented tool. It never names the closest sibling, get_metric_history, so the 'latest vs. history' distinction must be inferred from the word 'latest'.

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?

Usage is implied rather than stated: the agent can infer this is the one-network snapshot tool, and the behavior notes (metric_filter narrows, stale omitted unless include_stale) hint at narrower read patterns. However, there is no explicit 'use this instead of X when Y' guidance, and the pointer to find_networks for resolving tickers lives only in the schema, not the description.

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