petropt/petro-mcp

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

With no annotations provided, the description carries full burden and does well: it discloses the Monte Carlo simulation behavior, sampling distributions (lognormal/normal), Arps hyperbolic decline computation, and probabilistic output format. It doesn't mention performance characteristics like runtime or memory usage for 10k simulations, but covers core behavioral traits adequately.

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 first, followed by implementation details and parameter documentation. The Args section is well-structured but could be slightly more concise. Every sentence earns its place by adding necessary technical context.

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 (9 parameters, Monte Carlo simulation), no annotations, but with an output schema present, the description is quite complete. It explains the computational methodology, parameter meanings, and output format (probabilistic reserves with P10/P50/P90). The output schema will handle return values, so the description appropriately focuses on process and inputs.

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 fully. It provides detailed semantic explanations for all 9 parameters beyond just titles, including units (bbl/day, Mcf/day, 1/month), default values, distribution types, and their roles in the Monte Carlo process. This adds substantial value over the bare schema.

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 specific verbs ('Monte Carlo EUR estimation', 'Samples...computes...returns') and resources (qi, Di, b-factor, EUR). It distinguishes from siblings like 'calculate_eur' by specifying probabilistic reserve estimates with P10/P50/P90 outputs and Monte Carlo methodology.

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 'reserve report ready' and the technical parameters, but doesn't explicitly state when to use this tool versus alternatives like 'calculate_eur' or 'eur_distribution'. No guidance on prerequisites, exclusions, or comparative scenarios is provided.

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