petropt/petro-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool fits a model but doesn't describe what the output looks like (though an output schema exists), whether it's a read-only or mutating operation, error handling, or performance considerations. For a modeling tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 appropriately sized and front-loaded, starting with the core purpose. The technical context about the Duong model is relevant, and the parameter explanation is structured under 'Args:' for clarity. It avoids unnecessary fluff, though the model background could be slightly condensed to improve focus.

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's complexity (fitting a decline model), the description provides good context: purpose, model background, and parameter details. With an output schema present, it doesn't need to explain return values. However, without annotations, it could benefit from more behavioral details (e.g., computational requirements or validation steps) to be fully complete.

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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains that 'production_data' is a list of dicts with specific keys ('time' and 'rate' or 'oil'/'gas'), clarifies time units (months), and notes assumptions (sequential months). This compensates well for the schema's lack of details, though it doesn't cover all possible edge cases or formats.

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: 'Fit Duong decline model to production data using petbox-dca.' It specifies the verb ('fit'), resource ('Duong decline model'), and input ('production data'), and provides context about the model's application to fracture-dominated flow in unconventional reservoirs. However, it doesn't explicitly differentiate from sibling tools like 'fit_decline' or 'fit_ple_decline', which prevents a perfect score.

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 usage by stating the Duong model is 'designed for fracture-dominated flow in unconventional/shale reservoirs,' suggesting it should be used for such scenarios. However, it doesn't provide explicit guidance on when to choose this tool over alternatives like 'fit_decline' or 'fit_ple_decline', nor does it mention any prerequisites or exclusions, leaving usage context somewhat vague.

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