memory_detect_contradictions

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 the tool's two-step process (semantic search, then LLM reasoning), default behavior for edge creation, and the return format. However, it lacks details on rate limits, error handling, or performance characteristics that would be helpful for an agent.

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 well-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place by explaining functionality, parameters, or outputs without redundancy. The formatting with bullet-like sections enhances readability.

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 (3 parameters, no annotations, but with an output schema), the description is largely complete. It covers purpose, process, parameters, and returns, though it could benefit from more behavioral context like error cases or limitations. The output schema reduces the need to detail return values, but some operational guidance is missing.

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 0%, so the description must compensate. It provides meaningful context for all three parameters: 'memory_id' is explained as the memory to check, 'similarity_threshold' is given a default and purpose, and 'create_edges' is clarified with its default and effect. This adds significant value beyond the bare schema.

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 ('detect memories that contradict a given memory') and distinguishes it from siblings by focusing on contradiction detection rather than storage, recall, or analysis. It specifies the two-step process (semantic search + LLM reasoning), making the purpose unambiguous.

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 for contradiction detection but doesn't explicitly state when to use this tool versus alternatives like 'memory_check_supersedes' or 'memory_validate_tool'. No guidance is provided on prerequisites, edge cases, or exclusion criteria, leaving the agent to infer context from the purpose alone.

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