Review Agent Learnings

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

No annotations are provided, so the description carries the full burden. It clearly frames the operation as observational ('See everything') and lists the kinds of stored learning content, including 'unresolved gaps' and 'usage stats', which gives the agent a good sense of what to expect. It does not explicitly say 'read-only' but the verb is unambiguous and no mutating behavior is implied.

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 two sentences, front-loaded with the main purpose, and every word earns its place. It packs a clear value proposition and a use-case hint without redundancy or fluff.

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 simplicity (two optional params, no output schema, no nested objects), the description adequately conveys what the tool returns and why it matters. It could mention that results are filterable via section_filter or that the default view is an overview, but those details are already present in the input schema, so the description is complete enough for tool selection.

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's list of categories roughly maps to the section_filter enum values but adds little beyond what the enum and property descriptions already state. It does not explain the 'search' parameter beyond the schema, so it only marginally enhances parameter understanding.

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 uses the specific verb 'See' and identifies the resource as 'everything the agent has learned', then enumerates distinct content categories (knowledge updates, research findings, corrections, unresolved gaps, custom rules, usage stats). This clearly distinguishes it from sibling writing tools like update_knowledge and log_research.

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 context for when to use the tool: 'Use this to understand how the agent has evolved and what still needs attention.' It does not explicitly state when not to use it or name alternative tools, but the read-only nature relative to sibling logging/update tools is implied.

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