memory_clusters

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

No annotations exist, so the description must provide behavioral context. It explains the algorithm parameter and returns clusters with member IDs, sizes, and common tags. However, it does not disclose potential side effects, performance implications, or dependency on embeddings for Louvain.

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: one introductory sentence and a bulleted list of args. Every line adds value, no redundancy.

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 no annotations and an output schema (which may describe returns), the description covers parameters, defaults, algorithm behavior, and return structure. Could mention embedding requirements or cluster count limits, but overall suitably complete.

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?

Input schema has 0% description coverage. The description adds essential meaning: defines the role of min_cluster_size and min_score, and explains the difference between algorithms, including that Louvain uses embedding similarity.

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 'Detect clusters' and clearly states the resource ('related memories'). It distinguishes from sibling tools like memory_related (which likely finds related memories for a given memory) and memory_insights (which might provide broader analysis).

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?

No explicit guidance on when to use this tool versus alternatives like memory_related or memory_hybrid_search. It does not specify prerequisites or when clustering is appropriate.

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