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. It discloses key behavioral traits: the tool fits distributions, returns percentiles, parameters, and goodness-of-fit statistics. However, it lacks details on error handling, computational limits, or assumptions about input data quality. The description adds value but is not comprehensive.

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 core purpose, followed by concise details on inputs and outputs. Every sentence adds value without redundancy, and the structure is clear with a brief introduction and a parameter section.

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 (statistical fitting with 2 parameters), no annotations, and an output schema present, the description is reasonably complete. It covers purpose, inputs, and outputs, but could benefit from more behavioral context (e.g., handling of invalid data). The output schema likely details return values, reducing the need for that in the description.

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. It explains both parameters: 'eur_values' as a list from specific sources and 'distribution' with allowed types. It adds meaning beyond the schema by clarifying the purpose and options, though it could detail format constraints (e.g., numeric ranges).

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 ('Fit a statistical distribution to EUR values') and the resource ('EUR values for P10/P50/P90'). It distinguishes from siblings by focusing on distribution fitting rather than calculation, decline analysis, or other statistical methods present in the sibling list.

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 by mentioning input sources ('Monte Carlo, bootstrapping, or analog wells') and distribution types, but does not explicitly state when to use this tool versus alternatives like 'mc_eur' or 'prob_forecast' from the sibling list. No exclusions or prerequisites are provided.

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