memory_feeling_of_knowing

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

No annotations are provided, so the description carries full burden. It fully discloses behavioral traits: the tool is non-authoritative, cheap (no embedding call or graph query), returns a verdict with breakdown, and its calibration is still being measured. This provides rich transparency beyond what annotations might offer.

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 at three sentences, front-loading the key purpose and constraints. Every sentence adds value, with no redundancy or fluff. It is well-structured for quick comprehension.

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?

Although there is no output schema, the description adequately explains the return value (a verdict with signal breakdown and raw score). It covers all necessary aspects for an agent to decide when to use this tool, including its limitations and relation to memory_checkout.

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%, so baseline is 3. The description adds value by explaining cues as 'encoding-specificity cue fields' probed alongside query terms, which enriches understanding beyond the schema's 'Optional encoding-specificity cue fields' description. This justifies a slight increase.

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: an experimental metamemory pre-check to predict whether memory_checkout would return something for a query. It uses specific verbs ('predict') and resource ('memory_checkout result'), and distinguishes from sibling tools by emphasizing it is cheap, non-authoritative, and based only on in-memory session state.

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 provides clear usage context: use this tool for a quick, cheap prediction about memory_checkout. It explicitly advises 'when in doubt, call memory_checkout', giving a decision rule. However, it does not list specific scenarios where this tool should or should not be used compared to siblings, though the context is implied.

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