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

With no annotations, the description carries the full burden and does well: it discloses that the brain is shared across chats, devices and teammates, that an empty result means no brain exists yet, and that anchor ids are meant to be passed as reference_image_ids. It does not address permissions, error behavior, or size/response characteristics for very large brains, which keeps it short of a 5.

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?

Front-loaded with the read action and its contents, then the when-to-call, then the disambiguation cues. The long em-dash inventory of brain contents is dense but each element maps to a distinct part of the return value; it could be trimmed slightly but nothing is filler.

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?

There is no output schema, so the description must describe the return shape — and it does, enumerating look-lock, rules, locked subjects with anchor ids, and per-project plus brand-wide decisions with ids. Combined with the call-timing guidance and the empty-state note, an agent has everything needed to call it 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 description coverage is 100% and both parameters carry detailed guidance (minting the p- id, reusing it, renaming via project_name), so the schema already does the heavy lifting. The description adds nothing about project_id semantics beyond referring to "this project's" decisions and "the brand's other projects," so baseline 3 applies.

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 and resource ("Read this brand's emero brain in full") followed by an explicit inventory of what the brain contains: look-lock sheet, style/model leanings, defaults, graded rules, locked subjects with anchor ids, and decisions. It separates itself from siblings by naming get_settings ("short version"), save_to_emero_brain (growth), and load_skill (shape).

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?

Gives an explicit trigger — "Call it before the first generation for a brand" — plus the rationale (anchors and look-lock reused for consistency). It also routes the agent to alternatives for adjacent needs: get_settings for the short version, save_to_emero_brain for writing, load_skill for the data shape.

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