brain_wiki

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

The description discloses the full pipeline: retrieves up to k excerpts, synthesizes a structured page, writes to the wiki directory, and indexes immediately. It also mentions the dependency on a working LLM. With no annotations provided, this is strong behavioral disclosure, though it doesn't mention potential side effects like overwriting an existing page.

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 compact and front-loaded: it states the core action first, then the behavior, then usage. Every sentence earns its place, and the structure makes it easy for an agent to parse quickly.

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 tool with 2 params, 100% schema coverage, and no output schema, the description covers the key context: what it does, how it behaves, when to use it, and a prerequisite. It doesn't describe the return value, but with no output schema and a clear write-action, that's a minor gap. The lack of mention of overwriting behavior is a small omission.

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 schema already documents both parameters. The description adds context for k ('how many source excerpts to distill') and topic ('the topic to write the page about'), but these are already in the schema. The description's mention of 'up to k source excerpts' adds a small behavioral nuance, but not enough to push beyond baseline 3.

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: generating a wiki page by distilling index knowledge into a curated, cross-linked markdown page. It distinguishes itself from siblings by naming the memory-consolidation layer and the specific output artifact (wiki page), which is unique among the listed brain_* tools.

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 says when to use it ('when scattered notes about a topic should become one stable, searchable page') and even covers the regeneration use case ('regenerate the same topic to update it'). It also notes the requirement of a working LLM, which is a clear prerequisite.

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