memory_validate_tool

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: how confidence is adjusted based on success/failure (including formulas and min/max bounds), automatic promotion to GOLDEN_RULE at a confidence threshold of 0.9, and the return structure. This provides comprehensive insight into the tool's behavior beyond basic input/output.

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 behavioral details and parameter explanations. While efficient, the bullet points for success/failure outcomes could be slightly more integrated, but overall it avoids waste and maintains clarity.

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?

For a tool with 3 parameters, no annotations, and an output schema, the description is complete enough. It covers purpose, behavioral rules (confidence adjustments, promotion logic), parameter semantics, and hints at return values, providing sufficient context for an agent to use the tool effectively without needing to rely solely on structured fields.

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?

Given 0% schema description coverage, the description compensates fully by explaining all three parameters: memory_id identifies the target, success indicates application outcome, and adjustment defines the base confidence change with a default value. This adds essential meaning beyond the bare schema, clarifying each parameter's role in the validation process.

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 ('validate a memory', 'adjust its confidence score') and distinguishes it from sibling tools like memory_apply_tool or memory_analyze_health by focusing on validation and confidence adjustment rather than application or health 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?

The description implies usage context through the explanation of success/failure outcomes and confidence adjustments, suggesting it should be used after memory application to evaluate usefulness. However, it does not explicitly state when to use this tool versus alternatives like memory_outcome_tool or validation_history_tool, nor does it provide exclusions.

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