find_duplicates

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

With no annotations, the description takes on full disclosure responsibility. It explains the method (cosine similarity) and output (clusters with suggestions), but does not state whether the tool is read-only or if it performs any modifications. The phrase 'clean up' could imply action, but the description clarifies it returns suggestions.

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 extremely concise with only two sentences, each serving a distinct purpose: first explaining what the tool does, second recommending when to use it. No extraneous information.

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 absence of annotations and output schema, the description adequately covers purpose, method, and use case. It could be slightly more complete by explicitly noting that the tool does not modify memories (only returns suggestions), but overall it is sufficient for an AI agent.

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% with well-described parameters. The description adds no additional context beyond the schema for the three parameters, so it meets the baseline for parameter semantics without adding extra value.

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 the tool's action ('Find duplicate and near-duplicate memory clusters'), the method ('using cosine similarity on embeddings'), and the output ('returns clusters with suggested actions'). It is specific and uniquely identifies the tool among siblings.

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 explicitly recommends using the tool 'to clean up redundant memories', providing clear context. However, it does not mention when not to use it or suggest alternatives (e.g., merge_candidates, contradictions), though the tool is niche enough that this is less critical.

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