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Glama

climate

by io.github.pipeworx-io

Compare Models

compare_models
Read-onlyIdempotent

Compare temperature projections across climate models for the same location and date range. Returns side-by-side daily mean temperatures to assess model agreement and uncertainty.

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 temperature projections from multiple models
unitsYesUnits for each daily variable
modelsYesList of climate models compared
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 temperature projections from multiple models",
+      "items": {
+        "properties": {
+          "date": {
+            "description": "Date in YYYY-MM-DD format",
+            "type": "string"
+          },
+          "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"
+    },
+    "models": {
+      "description": "List of climate models compared",
+      "enum": [
+        [
+          "EC_Earth3P_HR",
+          "MPI_ESM1_2_XR",
+          "FGOALS_f3_H"
+        ]
+      ],
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "timezone": {
+      "description": "Timezone identifier for the location",
+      "type": "string"
+    },
+    "units": {
+      "additionalProperties": {
+        "type": "string"
+      },
+      "description": "Units for each daily variable",
+      "type": "object"
+    }
+  },
+  "required": [
+    "latitude",
+    "longitude",
+    "timezone",
+    "units",
+    "days",
+    "models"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "end_date": "2040-12-31",
+    "latitude": 35.6762,
+    "longitude": 139.6503,
+    "start_date": "2040-01-01"
+  },
+  {
+    "end_date": "1980-09-30",
+    "latitude": -33.8688,
+    "longitude": 151.2093,
+    "start_date": "1980-07-01"
+  }
+]
  3. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the tool's safety profile. The description adds that it returns side-by-side daily mean temperatures, which is useful output context, but does not disclose other behavioral traits such as data sources, model count, or error handling. This is modest extra value beyond annotations.

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?

Two sentences with no filler: the first states the action and scope, the second describes the output and purpose. Every word earns its place.

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?

The description is adequate for a read-only query tool with a rich annotation set and output schema. It covers purpose and return value, and the schema handles parameter constraints, but it doesn't mention how many models are compared or any caveats, which is acceptable given the context signals.

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 already describes all four parameters with 100% coverage, including date format and valid range. The description's reference to 'same location and date range' adds no new parameter-level detail, so the baseline of 3 applies.

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 ('Compare') and resource ('temperature projections across climate models') with scope ('same location and date range'), clearly distinguishing it from siblings like compare_entities. It also states the output (side-by-side daily mean temperatures), leaving no ambiguity.

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 gives clear context—comparing climate models for a location and date range—but does not explicitly mention when not to use it or name alternative tools. It implies a specific use case but lacks explicit exclusions or alternatives, so it falls short of a 5.

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