audit

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

The description correctly indicates a read operation consistent with readOnlyHint annotation. It adds value by stating the return format (JSON string) and parameter constraints, but behavioral traits are adequately covered by annotations.

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 concise and well-structured with Args and Returns sections. Every sentence is informative and there is no extraneous content.

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 presence of an output schema, the description sufficiently explains the return value. The two parameters are fully described, providing complete guidance for an agent to use the tool correctly.

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?

Despite 0% schema description coverage, the description explains both 'limit' (max entries, default 20) and 'operation' (optional filter by type). This compensates for the missing schema descriptions effectively.

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 'Get the audit trail of memory operations', using a specific verb ('Get') and resource ('audit trail'). It effectively distinguishes from sibling tools like 'add_triple' and 'remember' which are mutation operations.

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 defaults and filter options but lacks explicit guidance on when to use this tool versus alternatives. No context about when not to use it or comparison with similar tools like 'replay_diff'.

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