Ask the project brain a question (whole-brain, correction-aware answer)

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

No annotations exist, so the description carries the full behavioral burden. It discloses lexical ranking, conditional semantic ranking only when a local model is installed, graceful degradation on fresh installs, inclusion of superseded/archived history with flags, attachment of live corrections, and the fact that it returns synthesis-ready context rather than writing prose. This is far beyond minimal disclosure.

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 dense and fairly long, but every sentence adds meaningful information about behavior, usage, or outputs. It is front-loaded with the core purpose and supported by examples, ranking behavior, correction handling, and routing guidance. Slight trimming could improve readability, but the richness is justified for a complex 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?

Given the tool's complexity, absent annotations, and lack of an output schema, the description covers what an agent needs: question types, ranking behavior, historical context, correction awareness, return format (full cards + provenance + lifecycle), synthesis handoff, as_of semantics, and defaults. Nothing essential seems missing for correct selection and invocation.

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 100%, so the baseline is 3. The description adds extra meaning beyond the schema: it explains what as_of means behaviorally ('superseded cards count as live if they were current then'), gives concrete question examples for the question parameter, and clarifies the default canvas. This raises it above baseline, though k is only covered by its schema description.

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 a specific verb and resource: 'Answer a natural-language question from the WHOLE project brain,' with concrete example questions. It distinguishes itself from search_canvases by stating that brain_ask is for questions, not keyword lookups. This leaves no ambiguity about the tool's role among its siblings.

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 explicitly states when to prefer this tool: 'Prefer this over search_canvases when the user asks a QUESTION (not a keyword lookup).' It also names the alternative tool, explains the optional as_of parameter for historical questions, and notes the default canvas behavior. This is clear, actionable guidance.

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