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. It mentions the tool 'Uses petbox-dca for the forward model', which adds some implementation context, but does not disclose critical behavioral traits such as computational requirements, error handling, output format, or any limitations (e.g., data size constraints). For a modeling tool with no annotations, 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 details and parameter explanation are necessary for clarity. However, the formatting with line breaks could be slightly more streamlined, but it remains efficient 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 complexity of a decline modeling tool with no annotations, 0% schema coverage, but an output schema present, the description does well by covering the purpose, model context, and parameter details. The output schema likely handles return values, so the description focuses on inputs and usage, making it reasonably complete for agent selection.

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', explaining it as a list of dicts with specific keys ('time' and 'rate' or 'oil'/'gas') and units (months), which adds substantial meaning beyond the bare schema. This effectively documents the parameter despite the schema gap.

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 Power Law Exponential (PLE) decline model') and the target resource ('production data'). It distinguishes from siblings by specifying the PLE model and referencing Ilk et al., 2008, which differentiates it from other decline analysis tools like fit_decline, fit_duong_decline, and fit_sepd_decline.

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 context by mentioning 'tight/shale reservoirs' and 'transient and boundary-dominated flow regimes', suggesting when this model is appropriate. However, it does not explicitly state when to use this tool versus alternatives like bootstrap_decline or forecast_advanced_decline, nor does it provide exclusions or prerequisites.

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