forecast_material_consumption

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

With no annotations, the description carries the behavioral disclosure burden. It mentions combining stock and consumption, but does not disclose whether the tool is read-only, requires permissions, or has side effects. No rate limits or other constraints are mentioned.

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 reasonably concise with two short paragraphs and a parameter listing. The main purpose is front-loaded. However, the Args block uses Python docstring style, which is slightly verbose and non-standard for MCP, but still clear.

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 tool has only two simple parameters and no output schema, the description provides adequate coverage of inputs and behavior. However, it fails to describe the exact output format or structure, which is a gap since there is no output schema to rely on.

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?

The schema has no parameter descriptions (0% coverage), but the description adds meaningful explanations: material_type example 'PLA' and days_ahead default 30. This clarifies parameter purpose beyond the schema's minimal type and default information.

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 forecasts when a material type will run out, combining current stock with historical consumption. It specifies output: remaining days and urgency level. This distinguishes it from siblings like get_material_consumption_history and get_restock_suggestions which focus on past consumption or restocking.

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 use for forecasting depletion, but does not explicitly state when to use it versus alternatives or mention prerequisites like having stock data. No exclusions or alternative tools are mentioned, leaving the agent to infer usage context.

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