mc_eur

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

No annotations are provided, so the description fully shoulders the transparency burden. It explains the sampling process, distributions, and default values, but omits performance constraints, unit assumptions, and potential edge cases. The algorithm steps are clear enough for basic understanding.

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 a single paragraph with a clear introductory sentence and a bullet-style Args list. It avoids redundancy, but the list could be more compact. Overall, it is efficient and front-loaded.

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?

While the description covers input parameters well, it lacks specifics about the output schema. It mentions 'returns probabilistic reserve estimates' but does not detail the structure (e.g., dictionary with P10, P50, P90). Given the tool's complexity, this is a notable 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?

Schema description coverage is 0%, but the description provides detailed explanations for all 9 parameters, including meaning, units, and defaults. For example, 'qi_mean: Mean initial production rate (bbl/day or Mcf/day).' This far exceeds the schema's bare title and type.

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 performs Monte Carlo EUR estimation with P10/P50/P90, using Arps hyperbolic decline. It specifies the method and output purpose, distinguishing it from deterministic or decline-curve fitting tools like 'calculate_eur' or 'fit_decline'.

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?

No guidance on when to use this tool versus alternatives. For example, it doesn't explain when to prefer this over 'eur_distribution' or 'decline_sensitivity'. The sibling list includes many related tools, but the description offers no selection criteria.

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