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dogukanteber

netcdf-mcp

by dogukanteber

compare_variables

Compare two variables in a NetCDF file to check shape compatibility, range differences, and correlation. Identify mismatches and relationships for data quality assessment.

Instructions

Compare two variables for shape compatibility, range differences, and correlation.

Args: file_path: Path to the NetCDF file. variable1: Name of the first variable. variable2: Name of the second variable.

Returns: JSON string with comparison results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
variable1Yes
variable2Yes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

B3.3/5.0
Behavior3/5

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 clearly states the comparison dimensions and that it returns a JSON string, but it does not explicitly confirm the operation is read-only, describe error behavior, or mention what happens if variables are incompatible.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is tightly written and front-loaded with the core purpose. The Args and Returns sections provide necessary information without redundant or promotional language, and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is adequate for invoking the tool, but it lacks usage context, explicit safety assurances, and details about the comparison output beyond 'JSON string.' With no annotations and no visible output schema, slightly more context would make it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds basic meaning for all three parameters: file_path is a NetCDF path, variable1 is the first variable, and variable2 is the second. It does not add format details, validation constraints, or clarify that variable names must match file contents exactly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: compare two variables for shape compatibility, range differences, and correlation. This is distinct from sibling tools like get_variable_stats or get_variable_info, though it does not explicitly name a differentiating sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or sibling tools such as get_variable_stats or explain_variable that might perform related analyses.

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

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