brain_contribute_signal

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the privacy-safe nature, what data is shared, the confidence bucket mapping (high/medium/low from numeric input), and the aggregation condition for meta-lesson derivation. This provides good context beyond the schema. However, absent is any mention of return behavior, idempotency, or side effects like whether repeated calls are safe. Still, for a contribution tool, this is fairly transparent.

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 three sentences long, each with a clear purpose: first sentence states the core action and what is shared, second explains the aggregation mechanism, third gives usage guidance. There is no redundancy or filler. The most important information (purpose and privacy) is front-loaded. Every sentence earns its place.

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's moderate complexity (4 parameters, no output schema, no annotations), the description covers the essential aspects: what it does, what is shared, the privacy angle, and when to use it. The aggregation trigger is explained. It does not describe the return value or confirmation, but since no output schema exists, this is not a critical gap. The description is complete enough for an agent to decide when and how to invoke 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?

The input schema covers all 4 parameters with full descriptions, so baseline is 3. The description adds value by explaining that the confidence parameter is 'bucketed before sending' into high/medium/low categories, and that topic_category should be normalized (e.g., 'auth:jwt'). This context goes beyond the schema's basic type and enum descriptions, earning a 4.

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: 'Contribute a privacy-safe signal to the global Brain commons.' It specifies exactly what is shared (topic category, outcome, confidence bucket) and what is not shared (lesson text, org identity). It also distinguishes from the sibling tool fedbrain_contribute by explicitly noting the privacy requirement, making its role unambiguous.

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 explicit guidance: 'Use this instead of fedbrain_contribute when privacy is required (enterprise, GDPR).' This clearly indicates the context for using this tool versus the alternative. However, it does not explicitly state when not to use it (e.g., when privacy is not a concern, use fedbrain_contribute), which would strengthen the guidance. Still, the alternative is named and the condition is clear, earning a 4.

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