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 describes the tool's function and output purpose but lacks details on behavioral traits like error handling (e.g., what happens if arrays have mismatched lengths), performance characteristics, or any side effects. It does not contradict annotations, but for a tool with no annotations, more behavioral context would be beneficial.

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 and concise, with no wasted words. It starts with the core purpose, explains the effect, and lists parameters with clear explanations. Every sentence earns its place, making it easy to understand quickly.

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 (mathematical normalization), no annotations, 0% schema coverage, but with an output schema (implied by 'Has output schema: true'), the description is mostly complete. It covers purpose, usage, and parameter semantics effectively. However, it could improve by mentioning the output format or any assumptions (e.g., that arrays must be of equal length), but the presence of an output schema mitigates this gap.

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?

The schema description coverage is 0%, so the description must compensate fully. It does so by clearly explaining each parameter: 'rate: Production rates (bbl/d or Mcf/d).', 'flowing_pressure: Bottomhole flowing pressures (psi).', and 'initial_pressure: Initial reservoir pressure (psi).' This adds essential meaning beyond the schema, specifying units and roles, which is critical for correct usage.

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 with a specific formula (q / (Pi - Pwf)) and explains what it achieves: 'Normalize production rate by pressure drawdown' and 'Removes the effect of variable flowing pressure from production data, making it suitable for type curve analysis and RTA.' This distinguishes it from siblings like 'rta_blasingame' or 'rta_material_balance_time' by focusing on normalization rather than other RTA methods.

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 clear context for when to use this tool: 'making it suitable for type curve analysis and RTA.' It implies usage in reservoir engineering contexts where pressure-normalized rates are needed. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as 'rta_agarwal_gardner' or 'rta_npi', which might be used for different RTA techniques.

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