Space Weather Data MCP Server

describe_product

Fetch the full NOAA description for a space weather product by its slug to get interpretation guidance, methodology, and caveats.

Instructions

Fetch the full description of a SWPC product from the NOAA website.

Use list_products() to find product slugs. This tool fetches the live product page from www.swpc.noaa.gov and returns its explanatory text — useful for detailed interpretation guidance, methodology, and caveats.

Args: product_slug: The product URL slug, e.g. "planetary-k-index", "goes-x-ray-flux", "wsa-enlil-solar-wind-prediction".

Returns: Plain text extracted from the product's NOAA web page.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "product_slug": "<product_slug>"
    },
    "name": "describe_product"
  }
}

Input Schema

Name	Required	Description	Default
`product_slug`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A4.8/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It states the tool fetches a live product page and returns plain text, which implies a read-only network operation. It does not detail error handling or auth, but the core behavior is transparent. It slightly falls short of a 5 by not explicitly mentioning it does not modify anything, but 'fetch' strongly implies that.

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 well-structured with a clear opening sentence, a usage note, and explicit Args/Returns sections. Every sentence contributes value: purpose, prerequisite, behavior, and return format. It is appropriately sized for the tool's moderate complexity without wasted words.

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 description is complete given the tool's simplicity: one parameter, no nested objects, and an output schema (which covers return values). It explains what the tool does, how to use it, what input to provide, and what output to expect. It also provides contextual rationale ('useful for detailed interpretation guidance'). No significant gaps remain.

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?

Schema description coverage is 0%, so the description fully compensates. The only parameter, product_slug, is thoroughly explained: 'The product URL slug, e.g. "planetary-k-index"...' with concrete examples. This adds meaning well beyond the bare schema definition.

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 clearly states the tool's function: 'Fetch the full description of a SWPC product from the NOAA website.' This specifies the verb (fetch), resource (SWPC product description), and outcome (full description). It distinguishes from siblings like list_products (which lists products) and get_data_file_info (which fetches data), making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description explicitly advises to 'Use list_products() to find product slugs,' naming a sibling tool for a prerequisite step. It also clarifies when this tool is useful: 'for detailed interpretation guidance, methodology, and caveats.' This gives clear context and a direct alternative, meeting the 'when to use' requirement.

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

Deploy Server