petropt/petro-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's function (forecasting), the models used, and key behavioral aspects like default values (forecast_months: 360, economic_limit: 5.0) and optional parameters. However, it lacks details on error handling, performance characteristics, or output format specifics, which could enhance transparency further.

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 well-structured and front-loaded, starting with the core purpose, followed by usage guidelines, and then a detailed parameter breakdown. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse for an AI agent.

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?

For a tool with 4 parameters, 0% schema coverage, no annotations, but an output schema, the description is complete. It covers the tool's purpose, usage context, and all parameter semantics thoroughly. The presence of an output schema means return values need not be explained, and the description adequately addresses the complexity and input requirements.

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?

Given 0% schema description coverage, the description compensates fully by detailing all parameters. It explains 'model' options (ple, duong, sepd, thm), specifies the structure of 'parameters' for each model type, and clarifies 'forecast_months' and 'economic_limit' with defaults and units. This adds significant meaning beyond the bare schema, ensuring all parameters are well-understood.

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: 'Forecast production using an advanced decline model' and specifies the exact models (PLE, Duong, SEPD, THM) and outputs (rate-time forecast and cumulative production). It distinguishes itself from siblings like fit_ple_decline by focusing on forecasting rather than parameter fitting, making the verb+resource specific and differentiated.

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 explicit guidance on when to use this tool: 'Use parameters from fit_ple_decline, fit_duong_decline, or fit_sepd_decline, or provide THM parameters directly.' This clearly indicates the tool's dependency on prior fitting tools or direct parameter input, offering clear alternatives and prerequisites for usage.

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