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. While it states the calculation method and formula, it doesn't disclose important behavioral aspects like: whether this is a read-only calculation (implied but not stated), what units are expected/returned, error handling for invalid inputs, or any limitations of Barlow's formula. The description provides the core functionality but lacks operational context.

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?

Perfectly structured and concise. The first sentence states purpose and method, followed by the exact formula, then parameter definitions in a clean 'Args:' section. Every sentence earns its place with zero wasted words, and the information is front-loaded with the most important details first.

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 (engineering calculation with specific formula), the description provides excellent parameter semantics and the calculation method. Since an output schema exists (mentioned in context signals), the description doesn't need to explain return values. However, it could benefit from more behavioral context about the calculation's assumptions or limitations to be fully complete for this technical domain.

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?

With 0% schema description coverage (titles only provide parameter names), the description fully compensates by providing clear semantic definitions for all three parameters: 'Minimum yield strength (psi)', 'Nominal wall thickness (inches)', and 'Outer diameter (inches)'. It even clarifies that yield strength is 'minimum' and wall thickness is 'nominal', adding valuable engineering context beyond basic parameter names.

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 ('calculate internal burst pressure') and the method used ('using Barlow's formula with API 12.5% tolerance'), including the exact formula. It distinguishes itself from sibling tools like 'calculate_collapse_pressure' and 'calculate_hydrostatic_pressure' by focusing specifically on burst pressure calculation.

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 through the mention of 'Barlow's formula' and 'API 12.5% tolerance', which suggests engineering/pipe pressure applications. However, it doesn't explicitly state when to use this tool versus alternatives like 'calculate_collapse_pressure' or provide any exclusion criteria or prerequisites.

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