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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains the return type (plain-English paragraph) and contents set, implying a non-mutating read operation. It does not explicitly state read-only or side effects, but the summary nature and 'Returns' wording are sufficient for a low-risk tool.

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 front-loaded with 'USE WHEN' and efficiently lists use cases and return content. The phrase 'The moat made visible' is stylistic but not strictly necessary. Sentences are purposeful and the structure is scannable.

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 simple summary tool with no output schema, the description adequately covers the return format and content areas. It also references the sibling raw-data tool for follow-up. It does not describe edge cases like empty brain, but given the simplicity, this is acceptable.

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 50% (only tenantId has a description). The tool description adds context by mentioning 'per-workspace brain', which implies workspaceId is the scoping parameter, but it does not explain how tenantId relates or how parameters combine. This is marginal added value beyond the schema, not full compensation.

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 function: returning a plain-English summary of the per-workspace brain. It lists specific content (launch count, channel performance, brand voice, rejections) and distinguishes itself from the raw-data sibling chieflab_brain_read.

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?

Explicitly provides trigger phrases ('what do you remember about my brand?', 'show me my brain', 'what have you learned?') and directs users to pair with chieflab_brain_read for raw structured data, effectively framing when to use this summary vs the alternative.

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