Open-Brain

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'LLM synthesis' which implies AI processing, but doesn't describe what the tool actually does operationally—such as how it gathers data, what 'recent' means, whether it's read-only or has side effects, or what the output format is. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 a single, concise sentence that efficiently states the tool's function. It's front-loaded with the core purpose and avoids unnecessary words. However, it could be slightly more structured by explicitly mentioning the lack of parameters or output details, but overall it's appropriately sized.

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 complexity implied by 'LLM synthesis' and the lack of annotations and output schema, the description is incomplete. It doesn't explain what data sources are used, how 'recent' is defined, what 'open loops' refers to, or what the output looks like. For a tool that likely processes thought-related data (based on sibling tools), more context is needed to guide effective use.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details beyond the schema, but since there are no parameters, this is acceptable. The baseline for 0 parameters is 4, as the description doesn't need to compensate for any schema gaps.

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 states the tool performs 'LLM synthesis of recent themes, open loops, and suggested next steps,' which gives a general purpose but lacks specificity about what resources or data it operates on. It doesn't clearly distinguish from siblings like 'review_stale' or 'serendipity_digest' that might involve similar review/synthesis functions. The purpose is understandable but vague regarding scope and inputs.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, frequency, or context for weekly reviews, nor does it differentiate from sibling tools like 'review_stale' or 'dedup_review' that might handle similar review tasks. Without any usage context, the agent must infer when this tool is appropriate.

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