Mnemex

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

With no annotations provided, the description carries the full burden. It discloses behavioral traits such as clustering based on semantic similarity (if embeddings enabled) and returning clusters or duplicate pairs with scores and suggested actions. However, it lacks details on permissions, rate limits, or side effects (e.g., whether clustering modifies data).

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 appropriately sized and front-loaded, starting with the core purpose, followed by implementation details, and ending with parameter and return explanations. Every sentence earns its place without redundancy, and the structure (purpose → behavior → args → returns) is logical and efficient.

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 tool's moderate complexity (5 parameters, no annotations, but with output schema), the description is largely complete. It covers purpose, behavior, parameters, and return values. However, it lacks explicit guidance on when to choose clustering vs. duplicate finding, and the output schema existence means return details are not strictly needed, but some behavioral context (e.g., side effects) could be enhanced.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains all 5 parameters with clear semantics: 'strategy' as clustering strategy, 'threshold' for similarity linking, 'max_cluster_size' for cluster limits, 'find_duplicates' toggles between clustering and duplicate finding, and 'duplicate_threshold' for duplicate detection. This fully compensates for the schema's lack of descriptions.

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 purpose with specific verbs ('cluster similar memories', 'find duplicates') and resources ('memories'), distinguishing it from siblings like 'consolidate_memories' (which likely acts on clusters) and 'search_memory' (which finds individual memories). It explicitly mentions two distinct use cases: clustering for consolidation and finding duplicates.

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 clear context for when to use the tool ('useful for identifying redundant memories'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for consolidation or duplicate detection without specifying prerequisites or comparing to tools like 'consolidate_memories'.

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