get_brain_learnings

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

Without annotations, the description carries the burden of behavioral disclosure. It indicates a read operation ('Read'), which is helpful, but does not mention side effects, rate limits, or return behavior beyond listing data types.

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 concise at two sentences, front-loading the core purpose. However, the second sentence reads as an instruction rather than a structural element, slightly muddling clarity.

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?

With 24 parameters, no output schema, and no annotations, the description is insufficient. It does not explain the return format or how parameters like watch_sources or conclusion_statuses work, leaving significant gaps for an agent.

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 low (38%). The description adds minimal parameter context (e.g., audience filters with higher/lower observed efficiency). Many parameters like end_date, brand_name, start_date lack explanation, and the description does not compensate for the gaps.

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 reads one workspace's Brand Brain data, specifying the types of learnings included (observations, conclusions, watchouts, audience signals, gaps, agent_context). This verb+resource definition distinguishes it from similar 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 provides a usage context: 'Validate associations with controlled tests before changing allocation.' This implies when to use the tool, but it doesn't explicitly contrast with sibling tools like get_analysis or provide exclusion criteria.

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