auto_capture

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

Despite no annotations, the description discloses key behavioral traits: it uses heuristics (zero LLM calls), detects specific signal types, applies salience filtering, and results in durable memory storage. It does not cover auth or rate limits, but the core behavior is well explained.

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 concise (four sentences) and efficiently covers the tool's purpose, method, detection capabilities, outcome, and usage. No redundant or extraneous information.

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?

With no output schema, the description should clarify what the tool returns to the user. It mentions that items are stored as durable memories but does not describe the return value (e.g., confirmation or list). Overall, it is adequate but leaves minor gaps.

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 coverage is 100% with descriptive parameter names and descriptions. The tool description does not add extra parameter details beyond what the schema provides, so it meets the baseline but does not exceed it.

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 turns using lightweight heuristics, and it differentiates itself from siblings by emphasizing zero LLM calls and specific detection categories (preferences, identity facts, etc.).

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.' It provides clear context but does not discuss when not to use it or mention alternatives like distill_memory or store_memory.

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