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JimFlannery

Space Weather Data MCP Server

by JimFlannery

get_data_file_info

Retrieve field definitions and interpretation guidance for a space weather data file, explaining each field and how to interpret values before fetching.

Instructions

Return field definitions and interpretation guidance for a known data file.

Use this tool before calling fetch_file() to understand what the data means, what each field represents, and how to interpret the values.

Args: path: The file path as used with fetch_file(), e.g. "json/planetary_k_index_1m.json" or "json/goes/primary/xrays-1-day.json".

Returns: A dict with description, fields, interpretation, and related files. If the path is not in the guide, returns a list of known paths.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of disclosure. It explains the return structure (dict with description, fields, interpretation, related files) and the conditional behavior when a path is not found. This covers the main behavioral traits, though it omits details like error handling for malformed paths or access restrictions.

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 concise and well-organized: purpose, usage, argument, and return format. Every sentence adds value, and the structure is easy to scan.

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

Completeness5/5

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

The tool has a single parameter, no annotations, and an output schema (though not detailed in the description). The description provides usage context, example paths, output structure, and fallback behavior, making it complete for selecting and invoking the tool correctly.

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

Parameters5/5

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

The schema provides only 'path' as a string with no description. The description compensates fully by explaining the path must be the same as used with fetch_file() and provides concrete examples ('json/planetary_k_index_1m.json'). This adds critical meaning beyond the schema.

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

Purpose5/5

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

The description begins with 'Return field definitions and interpretation guidance for a known data file,' which clearly states a specific action and resource. It also distinguishes itself from siblings by explicitly saying 'Use this tool before calling fetch_file()' and by mentioning it returns related files.

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

Usage Guidelines4/5

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

The description provides clear guidance on when to use the tool ('Use this tool before calling fetch_file() to understand what the data means'), but it does not explicitly mention alternatives or exclusions. The fallback behavior of returning known paths hints at alternatives but doesn't name sibling tools.

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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