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 mentions 'query' which implies read-only behavior, but doesn't explicitly state whether this is safe, whether it modifies files, or any error handling. It lists parameters but doesn't describe behavioral aspects like file format requirements, performance characteristics, or what happens with invalid inputs. For a tool with no annotation coverage, this is insufficient.

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 efficiently structured with a clear purpose statement followed by a well-organized parameter section. Every sentence earns its place: the first sentence defines the tool's core function and data structure, while the parameter documentation is necessary given the schema's lack of descriptions. No wasted words or redundancy.

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 that an output schema exists (context signals indicate 'Has output schema: true'), the description doesn't need to explain return values. The description covers the essential purpose and parameters well. However, as a data query tool with no annotations, it could benefit from mentioning expected behavior with large files or error conditions, though the parameter documentation is comprehensive.

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?

With 0% schema description coverage, the description fully compensates by providing clear documentation for all 4 parameters: file_path (absolute path), well_name (optional filter), start_date (YYYY-MM-DD format), and end_date (YYYY-MM-DD format). It specifies which are optional, the expected format for dates, and the purpose of each parameter beyond what the bare schema provides.

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: 'Query production data from a CSV file' with specific columns listed (date, well_name, oil, gas, water). This provides a specific verb ('query') and resource ('production data from a CSV file'). However, it doesn't explicitly differentiate from sibling tools like 'get_curves' or 'get_curve_values' which might handle similar data retrieval, so it doesn't reach the highest clarity level.

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 for analysis and calculations (e.g., 'analyze_trends', 'calculate_eur'), there's no indication whether this tool is for raw data extraction versus processed analysis. The description only explains what it does, not when it's appropriate.

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