mc_eur

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

The description discloses the sampling method (lognormal/normal distributions) and the Arps hyperbolic decline model, but lacks details on error handling, performance, or prerequisites. With no annotations, the description carries the full burden, but it covers the core algorithmic behavior 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 front-loaded with the main purpose and method, followed by parameter descriptions. It is relatively concise, though the parameter list is detailed and necessary. Could be slightly more compact, but overall efficient.

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, output schema exists) and no annotations, the description covers the purpose, method, and parameters well. It lacks details on output structure or edge cases, but the presence of an output schema reduces the burden. It is complete enough for effective use.

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?

Despite 0% schema description coverage, the description explains each parameter's meaning, including units (bbl/day, Mcf/day, 1/month) and defaults, adding significant context beyond the schema's titles and types. All 9 parameters are described meaningfully.

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 'Monte Carlo EUR estimation with P10/P50/P90 (reserve report ready)', specifying the method, resource (EUR), and outputs. This clearly distinguishes it from sibling tools like 'calculate_eur' or 'eur_distribution' by emphasizing probabilistic Monte Carlo simulation for reserve reporting.

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 use for probabilistic reserve estimates but does not explicitly state when to use this tool versus alternatives like 'prob_forecast' or 'decline_sensitivity'. No when-not or exclusion guidance is provided.

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