@invoket/mcp

Get climate anomalies for a location

climate_anomaly

Assess climate anomalies by comparing observed temperature, precipitation, and wind against WMO 1991-2020 normals for any city or GPS point over a date range.

Instructions

Before your agent verifies a 'hotter than normal' claim, reports climate exposure or settles an anomaly-based trigger: how far observed temperature, precipitation and wind departed from the WMO 1991-2020 climatological normal, for any GPS point or city over a date range within the ingested Copernicus ERA5 window (rolling 30 years for temperature and precipitation, rolling ~5 years for wind). Price: $0.01 per call First call each day free (trial). Docs: https://invoket.com/docs/api/climate-anomaly

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": {
      "from": "<from>",
      "lat": "<lat>",
      "lon": "<lon>",
      "to": "<to>"
    },
    "name": "climate_anomaly"
  }
}

Input Schema

Name	Required	Description
`to`	Yes
`lat`	Yes	Latitude in decimal degrees. Alternative: pass city=<name> (alias location=, optional country= ISO 3166 alpha-2) instead of lat+lon — resolved via the embedded GeoNames gazetteer (exact match, most populous wins) and echoed back as data.location
`lon`	Yes
`from`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Aug 4, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses practical behavioral details like the price per call, first-call-free trial, 'rolling 30 years for temperature/precipitation and ~5 years for wind' data window, and links to docs. However, it doesn't disclose what the response/return format looks like (units, anomaly direction conventions, error cases like unmapped city) — moderate value but gaps remain on output behavior.

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 core description is one dense, front-loaded sentence covering data dimensions and normal window, followed by practical pricing/doc info. Pricing and doc link are arguably outside the tool's semantic description but useful. Slightly dense but well organized; no wasted 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?

Given the tool's complexity (multi-variable anomalies, rolling windows, city-or-coordinate addressing) and low schema coverage with no output schema, the description explains the anomaly semantics, normal baselines, and addressing alternatives well. It could detail the return format and error/edge behavior, but the core usage is adequately covered.

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?

Schema description coverage is only 25% — only 'lat' has a description. The description compensates substantially: it explains lat/lon can be replaced by city=<name> with aliases location=, country= ISO 3166 alpha-2, resolved via GeoNames with 'exact match, most populous wins' and echoed back as data.location. From/to necessity and the date range is implied. This adds meaningful semantics beyond the sparse 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 clearly states the tool computes how observed temperature, precipitation, and wind departed from the WMO 1991-2020 climatological normal for any GPS point or city over a date range. It has a specific verb (get/compute anomalies), resource (climate anomalies), and distinguishes scope. Sibling tools like climate_point (single point data), climate_aggregate, and climate_indices are differentiated by phrasing focused on anomaly-normal comparison, though it doesn't explicitly name siblings.

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 states 'before your agent verifies a hotter than normal claim, reports climate exposure or settles an anomaly-based trigger' which gives clear when-to-use context. It does not explicitly name alternative tools for when NOT to use it, but the meteorological anomaly verification intent is clear and specific enough to guide selection among the climate_* siblings.

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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