perseus_reflect

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

Discloses use of optional LLM and returns either grounded answer or memory context. With no annotations, the description carries the burden; it covers key behaviors without contradictions.

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?

Three front-loaded sentences with no filler. First sentence captures core purpose, followed by clear procedural explanation.

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?

Covers main behavior, LLM optionality, and return format. Lacks edge cases or error handling, but is adequate given tool simplicity and lack of output schema.

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%, but description adds value by explaining the roles of query and top_k in the reflection process, and the optional LLM configuration impact.

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?

Clearly states it synthesizes an insight from stored memories, using a question to recall relevant memories and produce a grounded answer. Distinguishes from sibling tools like perseus_recall (raw retrieval) and perseus_remember (storage).

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?

Explicitly describes when to use (given a question) and behavior when LLM is not configured. No explicit when-not or alternative mentions, but context implies use for synthesis vs. raw recall.

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