auto_capture

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

No annotations provided, so description carries full burden. It discloses key behaviors: zero LLM calls, heuristic-based filtering, salience-based storage. But it doesn't elaborate on side effects, error handling, or whether stored memories are immediately accessible.

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 two sentences: first defines purpose and function, second gives usage instruction. No redundant information, front-loaded, and each sentence adds value.

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 moderate complexity (3 params, no output schema), the description covers core functionality and usage. However, it lacks mention of return value or confirmation, which would help the agent understand the tool's output, especially since no output schema exists.

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%, with clear descriptions for text, scope, and source. The description does not add new information beyond what the schema already provides, so baseline 3 is appropriate.

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 extracts memory-worthy items from conversation text using lightweight heuristics, listing specific signal types. It distinguishes itself from sibling tools like store_memory by focusing on automatic extraction rather than direct storage.

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 explicitly says 'Use this when you want to analyze a block of conversation text and automatically capture any signals worth remembering,' providing clear usage context. However, it does not mention when not to use or compare to alternatives like store_skill or batch_store.

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