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

The description reveals key behavioral traits: cost-zero, no LLM involvement, deterministic, instant return. It explains the three return components (token, nearest tool, honest note) and how demand is aggregated via demand_radar with honest publication. Since annotations do not prohibit any side effects, this description fully informs about the tool's behavior without contradiction.

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?

The description is a single concise paragraph that front-loads the key idea ('ONE-FIELD survey wall') and efficiently covers purpose, return values, aggregation, and cost/determinism. Every sentence adds value, and it is appropriately sized for the tool's simplicity.

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 no output schema, the description fully covers return values and behavior. The 6 parameters are well-documented in the schema, and the description provides the necessary context for the tool's role in the ecosystem, including reference to demand_radar. It is complete for an agent to understand and use the tool 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 coverage is 100% with detailed descriptions per parameter. The description adds overarching context by emphasizing it is a 'ONE-FIELD' survey, explaining that multiple aliases (goal, intent, need) map to the same field. It also clarifies the purpose of 'nearest_to' and 'attributedTo' as hints and attribution. This adds meaning beyond the schema, though the schema already does well.

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 clearly states the tool's purpose as a 'survey wall' for registering intent, and explicitly lists what it returns: an intent token, nearest existing tool if match, or an honest note. It distinguishes from sibling tool demand_radar by noting that demand is aggregated there. The verb 'register' and resource 'intent' are specific, and the description sets it apart from other tools.

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 implies when to use it: to express an intent or find if a tool exists. It mentions that the intent token is required by some intent-gated tools, suggesting use before those. It also points to demand_radar for aggregated demand. However, it lacks explicit 'when-not-to-use' or direct alternatives beyond demand_radar, keeping it from a perfect score.

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