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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: de-identification, k-anonymity threshold (k>=10), and the fact that it costs DAC. However, it doesn't disclose other variables that might be relevant to a full understanding of the tool's behavior—like pagination limits or how deep the per-dimension counts go. The 'Costs DAC' note is a useful throughput/rate-limit hint that goes beyond the 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, all dense with meaning: what it does, privacy properties, and cost. Could perhaps add one note about whether it requires an environment context, but overall it's tight and front-loaded with the most critical 'what does this do' information first.

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?

Given the tool has a clear sibling distinction (anomaly vs. evidence report, environment observation), the description does enough by teaching what's special (k-anonymity, de-identified) without restating schema internals. It explains the DAC cost, the privacy guarantee, and the unit of analysis (across the population) well. A more complete description might warn about the DAC cost or mention that it's safe for production queries, but it's largely complete for an agent to use appropriately.

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 coverage is 100% and includes a payment/challenge flow, yet the description adds a high-level semantic: it's an observation aggregate across the population (not per-session). The description provides mental model context, especially for the 'dimension' parameter (returned distribution per dimension) and 'payment_signature' (the challenge flow is described in the schema; description adds 'De-identified, k-anonymity' context). Since the schema fully documents each param, the description's aggregation of them into a conceptual model earns above baseline.

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 starts with a specific verb+resource pairing ('Observe environment-level anomaly distribution') and clearly explains what the tool does: returns within_band/outlier counts per dimension across the population. It distinguishes itself from the sibling tool 'observe_environment' by specifying the anomaly distribution angle and the per-dimension breakdown across the population.

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?

The description clearly signals this is an environment-level aggregate tool (vs. individual or decision-level queries), which helps narrow when to use it. It also provides implicit exclusions by specifying what this tool is NOT for (de-identified aggregates, not detailed records). However, it doesn't explicitly name an alternative sibling tool or provide when-not-to-use conditions, which would elevate it to a 5.

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