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 describes the model's purpose and data requirements but lacks details on computational behavior (e.g., convergence, error handling), output format (though an output schema exists), or any side effects. The description does not contradict annotations, but it is insufficient for a tool performing a complex fitting operation.

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, with the core purpose stated in the first sentence. The additional context about the model and parameter details is relevant and efficiently presented. However, the formatting with line breaks could be slightly improved for readability, but it remains concise without wasted sentences.

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 covers the purpose, model context, and parameter semantics well. With an output schema present, it does not need to explain return values. The main gap is in behavioral transparency, but overall, it provides a solid foundation for an agent to understand and invoke the tool correctly.

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 detailed semantics for the single parameter 'production_data', specifying acceptable data structures (list of dicts with 'time' and 'rate' keys, or 'oil'/'gas' keys) and units ('months'). This adds significant value beyond the schema, which only indicates an array of objects without specifics.

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 specific action ('Fit Stretched Exponential (SEPD) decline model') and resource ('production data'), with explicit mention of the model's academic reference (Valko, 2009) and application domain ('unconventional reservoirs with heterogeneous fracture networks'). It distinguishes itself from sibling tools like 'fit_decline' or 'fit_duong_decline' by specifying the SEPD model type.

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 for unconventional reservoirs with heterogeneous fracture networks, providing some context, but does not explicitly state when to use this tool versus alternatives like 'fit_decline' or 'fit_duong_decline'. No exclusions or prerequisites are mentioned, leaving the agent to infer based on the model's applicability.

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