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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that: the result includes catalog brains external to the user, and with `shares: true` it exposes the share codes of the user's own brain – a data-disclosure nuance an agent should know before calling.

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 what the tool returns, then usage conditions, then the parameter nuance – a sensible order with little waste. The first sentence is quite dense and the parenthetical about Carlos Moya will go stale, slightly weakening efficiency.

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 read-only, zero-required-param listing tool with no output schema, the description conveys enough about the returned content (brains, their knowledge domains, usage rules) to set expectations. It stops short of describing structure or volume of the result, but nothing critical for correct invocation is missing.

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 0% for the single `shares` parameter, so the description must carry the burden. It does explain that `shares: true` additionally returns the codes with which the user shares their own brain, but it never explicitly states the default or what happens when the flag is omitted or false.

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?

It names a concrete verb plus resource ('dice qué otros cerebros puede usar el usuario') and enumerates the exact contents: connected brains, catalog brains (currently Carlos Moya's), what each one knows, and its usage rules. This makes it unambiguous versus siblings like search_brain or connect_brain.

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?

Two concrete triggers are given: call it before searching another brain for the first time in a conversation, and when the user asks which brains they have or who they can consult. It never names the alternative tool (search_brain) explicitly nor states when not to call it, so it falls short of the top score.

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