Search Long-Term Memory

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

No annotations are provided, so the description carries full burden. It mentions 'text matching' but doesn't disclose behavioral traits like whether this is a read-only operation, how results are returned (e.g., pagination, format), performance considerations, or error handling. For a search tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and avoids unnecessary elaboration, making it easy to parse quickly.

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 (search operation with 3 parameters), no annotations, but a rich input schema (100% coverage) and an output schema (present), the description is minimally adequate. The output schema likely handles return values, but the description lacks context on usage scenarios, behavioral traits, or integration with sibling tools, leaving gaps 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 description coverage is 100%, so the schema already documents all parameters (keyword, limit, caseSensitive) with their types and defaults. The description adds no additional meaning beyond what the schema provides, such as search algorithm details or examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 action ('search') and target resource ('memory nodes') with the mechanism ('by keyword (text matching)'). It distinguishes from siblings like 'getLongTermMemory' or 'getAssociations' by specifying search functionality, though it doesn't explicitly contrast with all alternatives.

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 guidance is provided on when to use this tool versus alternatives like 'getLongTermMemory' (which might retrieve specific memories) or 'getAssociations' (which might find related concepts). The description implies usage for keyword-based searching but offers no context about prerequisites, exclusions, or comparative scenarios.

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