brain_architecture

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 discloses the 8000-character truncation and name/basename substring matching, which are key behaviors. However, it omits edge cases like what happens when no document matches or when multiple documents match, leaving some ambiguity.

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 only two sentences, front-loading the core purpose and then describing parameter-dependent behavior. No fluff or redundancy; every clause contributes to understanding the tool.

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 tool with one optional parameter and no output schema, the description covers the essential operational modes and constraints like truncation. Minor gaps remain around error handling and multiple-match behavior, but the core functionality is well documented.

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 schema has no descriptions for the `area` parameter (0% coverage), but the tool description compensates by explaining its function: selecting a specific doc by name/basename substring. This adds meaningful semantic context that the raw schema lacks.

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 identifies the tool as providing curated architecture docs, differentiating it from sibling tools like brain_db_schema or brain_ml_models by domain. It also specifies two distinct behaviors (list vs. retrieve) based on the `area` parameter, which fully explains its purpose.

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 clear usage instructions for both modes: without `area` it lists doc names/sections/sizes, and with `area` it returns the doc content. It does not explicitly mention alternatives or exclusions, but the domain-specific naming makes when to use it obvious.

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