NatGasLens — US Natural Gas & Henry Hub Data

get_us_natural_gas_weather_models

Read-onlyIdempotent

Weather forecasts that drive US natural gas demand: 7-day population-weighted CONUS cooling and heating degree days from the GFS and ECMWF runs, the change versus the previous day's run, the GFS-ECMWF spread, the per-day degree-day profile, and the latest NOAA CPC forecast revision with its percentile. Full data on both scopes.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`asof`	No
`data`	No
`note`	No
`tool`	No
`scope`	No
`stale`	No
`disclaimer`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJul 28, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "asof": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null,
+      "title": "Asof"
+    },
+    "data": {
+      "additionalProperties": true,
+      "default": null,
+      "title": "Data",
+      "type": "object"
+    },
+    "disclaimer": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null,
+      "title": "Disclaimer"
+    },
+    "note": {
+      "anyOf": [
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ],
+      "default": null,
+      "title": "Note"
+    },
+    "scope": {
+      "default": null,
+      "title": "Scope",
+      "type": "string"
+    },
+    "stale": {
+      "default": null,
+      "title": "Stale",
+      "type": "boolean"
+    },
+    "tool": {
+      "default": null,
+      "title": "Tool",
+      "type": "string"
+    }
+  },
+  "title": "Envelope",
+  "type": "object"
+}

AddedJul 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context by enumerating the data fields, including the previous-day-run change, model spread, and CPC percentile. It explains what kind of data the agent will receive without relying on the output schema alone.

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

Conciseness4/5

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

The description is a single dense sentence that packs substantial detail into a compact form. It front-loads the core purpose and then lists the included data elements. The phrase 'Full data on both scopes' is slightly ambiguous, but overall the description is efficient with no filler.

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

Completeness4/5

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

For a parameterless read-only tool with an output schema and rich annotations, the description provides strong context about the data contents, model sources, and derived metrics. The slight ambiguity around 'both scopes' and the lack of any temporal or update-frequency context prevent a perfect score.

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

Parameters4/5

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

The tool has zero parameters, so there are no parameter semantics to document. The schema description coverage is complete by default, and the baseline for parameterless tools is 4 because the description rightly focuses on the returned data rather than input options.

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 identifies a specific resource: US natural gas demand weather models, listing exactly which datasets are included (7-day population-weighted CONUS degree days, GFS/ECMWF runs, spread, per-day profile, CPC revision). This differentiates it well from sibling tools focused on storage, production, LNG, or market data.

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

Usage Guidelines3/5

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

The description implies when this tool is relevant by stating it covers 'weather forecasts that drive US natural gas demand,' but it does not explicitly say when to use it versus alternatives like market briefs or fundamental scores. The usage context is clear but no exclusions or routing guidance is provided.

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

All NatGasLens — US Natural Gas & Henry Hub Data tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?