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

Since no annotations are provided, the description carries the full burden and does well by disclosing cost (1 DAC), authentication needs (auth_token), payment method (x402), and a critical restriction (Trial does not cover ARA observation). This is valuable behavioral context beyond a simple one-liner.

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 sentences, no fluff. Every word adds value, covering purpose, cost, auth, and payment context in under 40 words.

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 tool with no annotations and no output schema, the description covers the essential behavioral aspects: what it does, what it costs, auth requirements, and payment limitations. Additional info like return format would be nice but is often not critical for a straightforward observation tool.

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 the baseline is 3. The description adds no parameter-specific details beyond what the schema already contains, but it does add cost/trial context that helps interpret the payment_signature flow.

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 uses a specific verb ('Observe') and clearly defines the resource ('pattern-level EE distributions and action-type breakdowns across agents'). It is specific and distinct from the sibling tool 'observe_environment', which appears to be environment-focused.

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 provides usage context by mentioning cost, auth requirements, and trial limitations, but it does not explicitly state when to prefer this tool over alternatives like 'observe_environment', nor does it give explicit when-not-to-use guidance.

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