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 discloses key behavioral traits: it's a statistical analysis tool that resamples data, refits models, and returns confidence intervals. However, it doesn't mention computational intensity, error handling, or output format details. The description adds value but isn't comprehensive for a tool with no annotation coverage.

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: the first sentence states the core purpose, followed by a brief explanation of the method, then parameter details. Every sentence earns its place with no wasted words, making it efficient and well-structured.

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 3 parameters with 0% schema coverage and no annotations, the description does an excellent job explaining inputs and purpose. However, it doesn't describe the output (though an output schema exists, so this is mitigated). For a statistical tool with no annotation support, it's nearly complete but could mention output structure or error cases.

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 fully compensate. It provides clear semantics for all 3 parameters: 'production_data' structure, 'model' options with specific enums, and 'num_bootstrap' purpose with default. This adds substantial meaning beyond the bare schema, fully documenting parameter 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 specific verbs ('Bootstrap decline curve parameters from production data') and distinguishes it from siblings like 'fit_decline' or 'eur_distribution' by specifying it uses resampling with replacement to return confidence intervals. It's not a tautology and provides meaningful differentiation.

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 'production data' and 'decline model', but doesn't explicitly state when to use this tool versus alternatives like 'fit_decline' or 'mc_eur'. It provides some guidance through parameter descriptions but lacks explicit when/when-not directives or named alternatives.

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