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Glama

climate

by io.github.pipeworx-io

Get Climate Projection

get_climate_projection
Read-onlyIdempotent

Get historical and future temperature and precipitation projections for a location (1950–2050). Returns daily forecasts with temperature, precipitation, and weather conditions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateYesEnd date in YYYY-MM-DD format (must be between 1950 and 2050).
latitudeYesLatitude of the location in decimal degrees.
longitudeYesLongitude of the location in decimal degrees.
start_dateYesStart date in YYYY-MM-DD format (must be between 1950 and 2050).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysYesArray of daily climate projections
unitsYesUnits for each daily variable (e.g., °C, mm)
latitudeYesLatitude of the location in decimal degrees
timezoneYesTimezone identifier for the location
longitudeYesLongitude of the location in decimal degrees

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "days": {
+      "description": "Array of daily climate projections",
+      "items": {
+        "properties": {
+          "date": {
+            "description": "Date in YYYY-MM-DD format",
+            "type": "string"
+          },
+          "precipitation_sum": {
+            "description": "Total precipitation in mm",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "temperature_2m_mean": {
+            "description": "Mean temperature in °C",
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "required": [
+          "date"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "latitude": {
+      "description": "Latitude of the location in decimal degrees",
+      "type": "number"
+    },
+    "longitude": {
+      "description": "Longitude of the location in decimal degrees",
+      "type": "number"
+    },
+    "timezone": {
+      "description": "Timezone identifier for the location",
+      "type": "string"
+    },
+    "units": {
+      "additionalProperties": {
+        "type": "string"
+      },
+      "description": "Units for each daily variable (e.g., °C, mm)",
+      "type": "object"
+    }
+  },
+  "required": [
+    "latitude",
+    "longitude",
+    "timezone",
+    "units",
+    "days"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "end_date": "2020-12-31",
+    "latitude": 40.7128,
+    "longitude": -74.006,
+    "start_date": "2020-01-01"
+  },
+  {
+    "end_date": "2030-08-31",
+    "latitude": 51.5074,
+    "longitude": -0.1278,
+    "start_date": "2030-06-01"
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds behavioral context by specifying the return format ('daily forecasts with temperature, precipitation, and weather conditions') and the time range, but does not describe data sources or potential limitations. This adds value beyond annotations without contradicting them.

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 two sentences with no filler. The first sentence states the core purpose and scope, and the second states the return structure. Every word earns its place.

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?

For a read-only data retrieval tool with full annotations, a 100% schema-documented parameter set, and an output schema (as indicated), the description is sufficient. It states the temporal range, the data variables (temperature, precipitation, weather conditions), and the daily granularity. No major gaps are apparent.

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?

The input schema provides 100% coverage with descriptions for all four parameters (latitude, longitude, start_date, end_date). The description does not add additional parameter syntax or format details beyond what the schema already states, so baseline 3 is appropriate.

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 uses a specific verb ('Get') and clearly identifies the resource ('historical and future temperature and precipitation projections for a location'), including the temporal range (1950–2050) and return content ('daily forecasts with temperature, precipitation, and weather conditions'). This clearly distinguishes it from unrelated sibling tools.

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 clearly indicates when to use this tool: when needing climate projection data for a location within 1950–2050. However, it does not explicitly mention alternatives or exclusions, though no sibling tools appear to offer similar climate data functionality.

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

All climate toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Multiple query-router tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and bet_research all serve as entry points into the same underlying data, with ask_pipeworx_beta explicitly described as currently identical to ask_pipeworx. Prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have fuzzy boundaries that an agent could easily mis-select.

Naming Consistency3/5

Most tools follow a readable lower_snake_case verb_noun pattern (compare_entities, get_climate_projection, resolve_entity), but conventions are mixed: pipeworx_feedback, pipeworx_trending, recent_alerts, and recent_changes are noun-first/non-imperative, and the ask_pipeworx family uses a verb-plus-variant-suffix style. The pattern is predictable enough to navigate but not consistent.

Tool Count2/5

33 tools is heavy for a single MCP server, and the server name 'climate' does not match the broad data-research, prediction-market, memory, subscription, and web-utility scope actually covered. Several tools could be consolidated (ask_pipeworx variants, discover_tools/suggest_questions, multiple polymarket scanners), which would reduce cognitive load without losing capability.

Completeness4/5

As a general data-access and research surface, the tool set is quite complete: it covers lookup, grounded verification, deep research, entity resolution, comparisons, recent changes, memory, subscriptions, alerts, feedback, and tool discovery. Minor gaps exist — the climate-specific coverage is limited to projections and model comparison despite the server name, and there is no direct tool to page through the full catalog — but for its inferred broad purpose there are no major dead ends.

Resources