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petropt

petropt/petro-mcp

calculate_wellbore_tortuosity

Calculate the wellbore tortuosity index from survey data to quantify deviation from an ideal wellpath. Higher values indicate more tortuous paths impacting drilling and production operations.

Instructions

Calculate wellbore tortuosity index from survey data.

Tortuosity measures how much the wellbore deviates from an ideal path. Higher values indicate more tortuous wellpath, impacting drilling and production operations.

Args: md: List of measured depths. inclination: List of inclinations (degrees). azimuth: List of azimuths (degrees). unit: Depth unit -- 'feet' or 'meters'. Default 'feet'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mdYes
inclinationYes
azimuthYes
unitNofeet

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, and the description does not disclose any behavioral traits such as side effects, safety (read-only nature), or error handling. It fails to inform the agent that this is a pure computation with no external side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a clear purpose statement and an efficient Args list. However, it could be more structured by front-loading the purpose more prominently. It wastes no sentences but the Args section somewhat duplicates schema fields.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema, so return values are covered. The description explains tortuosity's impact, but lacks context on data constraints, error conditions, or any prerequisite understanding. It is adequate but not comprehensive for a calculation tool without annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage (only titles), but the description's Args section fully explains each parameter: md as list of measured depths, inclination/azimuth in degrees, and unit with allowed values and default. This adds complete semantic meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool calculates wellbore tortuosity index from survey data, with a concise explanation of what tortuosity measures. However, it does not explicitly distinguish itself from sibling tools like calculate_dogleg_severity, which could cause confusion for an AI agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 does not mention conditions for use, prerequisites, or scenarios where other tools might be more appropriate, leaving the agent without decision support.

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

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