get_crowd_intelligence

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

The [BETA] tag and network scaling disclosure add value beyond the empty schema, but with no annotations provided, the description carries the full burden and omits rate limits, data freshness, or caching behavior typical for intelligence feeds.

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 efficiently structured sentences: beta status and primary purpose front-loaded, return values specified second, and quality caveat third. Every sentence conveys distinct information without redundancy.

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?

Without an output schema, the description adequately enumerates the four aggregate metrics returned. It covers the beta lifecycle status and network dependency, though data update frequency or TTL would strengthen completeness.

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 zero parameters and 100% schema description coverage trivially satisfied, the description appropriately makes no parameter claims, meeting the baseline expectation for zero-argument tools.

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?

Specific verb 'see' and resource 'what other Fathom-connected agents are doing' clearly identifies the crowd intelligence scope. Lists specific return metrics (posture distribution, consensus strength, fear levels) that distinguish it from individual asset analysis tools like get_asset_context or get_sentiment_state.

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?

Provides the caveat that 'Signal quality scales with network size,' implying usage constraints, but lacks explicit guidance on when to prefer this over similar aggregate tools like get_sentiment_state or get_narrative_pulse in the sibling list.

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