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 of behavioral disclosure. While it mentions what the tool computes and flags, it lacks critical details: required permissions for file access, performance characteristics (e.g., processing time for large files), error handling (e.g., invalid CSV format), or output format hints. For a tool that reads files and performs analysis, this is a significant gap.

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 well-structured and front-loaded: the first sentence states the core purpose, followed by computed metrics, and then parameter details in a clear 'Args' section. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 (file-based analysis with anomaly detection), no annotations, and an output schema present (which reduces the need to describe return values), the description is moderately complete. It covers purpose and parameters well but lacks behavioral context (e.g., file handling, performance) and usage guidelines relative to siblings, leaving gaps for an agent to operate effectively.

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 adds meaningful context for both parameters: 'file_path' is clarified as an absolute path to a production CSV file, and 'well_name' is optional for filtering. This goes beyond the schema's basic titles, providing practical usage details. However, it doesn't specify CSV format requirements (e.g., column names, date formats).

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: analyzing production trends and detecting specific anomalies (shut-ins, rate jumps, water breakthrough, GOR blowouts). It lists the computed metrics (water cut trend, GOR trend, oil decline rate, cumulative production) and flags anomalous events. However, it doesn't explicitly differentiate from sibling tools like 'query_production' or 'fit_decline', which might have overlapping functionality.

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 provides no guidance on when to use this tool versus alternatives. With many sibling tools related to production analysis (e.g., 'query_production', 'fit_decline', 'forecast_advanced_decline'), there's no indication of context, prerequisites, or exclusions. The agent must infer usage based on the purpose alone.

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