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 and excels by detailing key traits: it specifies the return values ('fitted parameters, R-squared, and predicted rates'), constraints ('Physics-constrained: b-factor bounded to [0, 2], non-negative rates enforced'), and model-specific behaviors. This goes beyond basic functionality to inform the agent about output structure and limitations.

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 supported models, return values, constraints, and parameter details. Every sentence adds value 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?

Given the complexity of the tool (multiple models, physics constraints), no annotations, and an output schema present, the description is complete. It covers purpose, usage context, behavioral traits, and parameter semantics thoroughly. The output schema handles return value details, so the description appropriately focuses on other aspects without redundancy.

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 description coverage is 0%, so the description must compensate fully. It does so by clearly explaining both parameters: 'production_data' is defined with examples of key structures ('time' and 'rate' or 'oil'/'gas' keys), and 'model' lists all possible values with their meanings. This adds essential semantic context not present in the schema.

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 with specific verbs ('fit decline curves') and resources ('production data'), and it distinguishes itself from siblings by specifying the types of decline models supported (Arps, modified hyperbolic, Duong). This is more specific than generic sibling tools like 'analyze_trends' or 'fit_duong_decline' (which only handles one model).

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 for when to use this tool by specifying the supported models and their applications (e.g., 'Duong model for unconventional/shale wells with fracture-dominated flow'), which helps differentiate it from alternatives. However, it does not explicitly state when not to use it or name specific sibling alternatives (e.g., 'fit_duong_decline' for only Duong model fits), so it falls short of a perfect score.

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