capture_interaction

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 the three-tier extraction process (online LLM, offline LLM, heuristic fallback), which reveals internal behavior. However, it does not mention side effects, auth requirements, or failure modes.

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 three sentences, front-loaded with the purpose, and contains no redundant information. Every sentence contributes value: purpose, automatic logging, and extraction tiers.

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 simple schema and no output schema, the description explains the extraction process but omits important details like what the tool returns (e.g., success confirmation, memory ID) or how it interacts with sibling tools. It lacks completeness for a full understanding.

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%, meaning the input schema already fully documents both parameters. The tool description adds no extra meaning beyond what the schema provides, so it meets the baseline but does not enhance parameter understanding.

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 ('capture'), the resource ('conversation interaction as memory'), and the specific outcomes ('extracts learnable moments', 'auto-logs them'). It distinguishes itself from siblings like 'log_learning' and 'retrieve_learnings' by focusing on the initial capture step.

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 the tool is for storing raw conversation text as memory, but it lacks explicit guidance on when to use it versus alternatives like 'log_learning' or 'consolidate'. It does not specify prerequisites or scenarios where it is inappropriate.

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