petropt/petro-mcp

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

No annotations are provided, so the description carries full burden. It mentions the calculation methods but doesn't disclose behavioral traits like error conditions, numerical precision, assumptions, or what the output represents. For a complex engineering calculation with 6 parameters, this leaves the agent guessing about reliability 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 with a clear purpose statement, method details, and parameter documentation. Every sentence earns its place. It could be slightly more front-loaded by moving the Args section after the initial description, but overall it's efficiently organized without wasted words.

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 (engineering calculation with 6 parameters), no annotations, but with an output schema (implied by context signals), the description is partially complete. It explains parameters well but lacks behavioral context and usage guidance. The output schema existence means return values don't need explanation, but the description should still cover more about the calculation's nature and limitations.

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 description coverage is 0%, so the description must compensate. It provides clear semantic explanations for all 6 parameters in the Args section, defining units and optionality. This adds substantial value beyond the bare schema. The only minor gap is not explaining relationships between parameters (e.g., how bubble_point_pressure relates to pressure).

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: 'Calculate oil compressibility above and below bubble point.' It specifies the exact calculation (oil compressibility) and distinguishes between two pressure regimes. However, it doesn't explicitly differentiate from sibling tools like 'calculate_pvt_properties' or 'calculate_bubble_point', which might cover related calculations.

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 no guidance on when to use this tool versus alternatives. It mentions the methods used (Vasquez-Beggs and material-balance) but doesn't explain when this calculation is needed or what prerequisites exist. With many sibling tools in the oil/gas domain, the lack of comparative context is a significant gap.

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