curve_fit

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

No annotations are provided, so the description carries full burden. It does not disclose whether the tool is read-only, modifies memory, or requires a minimum history length. The phrase 'Personalize each memory's forgetting forecast' could be interpreted as a write operation, making behavior ambiguous.

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 a single sentence that packs in purpose, method, and model citation without wasted words. It is slightly dense but remains readable and front-loaded with the main action.

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 and no annotations, the description should explain return values and prerequisites. It does not state what the tool returns (only predicts days, not the format), nor does it address edge cases like insufficient history. The description is too brief for the tool's complexity.

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 0%, so the description must explain parameters. It gives context for 'threshold' ('crosses a threshold') but does not explain 'memory_id' (obvious but unstated) or 'horizon_days' (no mention at all). The compensation is partial at best.

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 a specific action: personalizing a memory's forgetting forecast from retrieval history and predicting days until a threshold is crossed. It distinguishes itself from siblings by emphasizing 'individual forgetting rates' and citing Murre & Chessa (2011), which implies a unique model compared to other forecast tools.

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 context: use when you have retrieval history for a memory and want an individualized forgetting curve prediction. However, it does not explicitly mention alternatives or when not to use, so it falls short of a 5.

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