audit_log

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

With readOnlyHint already present, the description adds useful context: the tool returns time-ordered history, covers all memory mutations, and serves integrity/debugging purposes. It does not contradict the annotation, but it omits details like ordering direction (e.g., newest first) and the exact shape of returned entries, so it is not fully transparent.

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 core purpose, then provides usage guidance and an explicit contrast with a sibling tool. Every sentence earns its place with no redundancy or filler.

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 read-only audit log with three optional filters, the description covers purpose, usage scenarios, and differentiation from semantic_recall. It doesn't specify the return format or ordering direction, but given the simplicity of the tool and the presence of readOnlyHint, these are minor gaps. The description is sufficiently complete for an agent to select and invoke it 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?

The input schema already documents all three parameters with descriptions (100% coverage). The description adds a concrete usage hint for last_n ('Pass last_n=1 for the single most recent mutation'), which goes beyond the schema's generic 'Number of recent entries to return (default 20)' and helps the agent apply the parameter correctly.

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 'Read the audit trail of all memory mutations,' identifying the specific verb (read) and resource. It also explicitly contrasts with semantic_recall, distinguishing its scope and purpose from a key sibling tool.

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?

It gives explicit guidance to 'Use this — not semantic_recall —' for recency questions, provides concrete example queries, and closes with a clear differentiation: semantic_recall for topic search, audit_log for time-ordered history, integrity checks, and debugging. This fully addresses when to use the tool vs. alternatives.

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