Gdelt

Timeline Volume

timeline_volume

Read-onlyIdempotent

Day-by-day SHARE OF GLOBAL NEWS attention for a query — what % of all worldwide articles mentioned this topic each day. Returns datapoints with timestamp and intensity (% of total news volume). Use to detect news-cycle spikes around events ("when did attention to X peak?"), benchmark attention against history, or pair with timeline_tone to chart sentiment vs interest together. Cheaper than search_articles when you only need the volume curve, not the source articles themselves.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	GDELT query string
`timespan`	No	Lookback window (default "1m")
`enddatetime`	No	YYYYMMDDHHMMSS — only with timespan=custom
`startdatetime`	No	YYYYMMDDHHMMSS — only with timespan=custom

Output Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	The query string used
`metric`	Yes	Metric type: volume_pct (% of news)
`points`	Yes	Number of datapoints returned
`series`	Yes	Day-by-day volume datapoints
`timespan`	Yes	Lookback window applied (default 1m)

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedMay 22, 2026

addedInput schema / examples

Added value: +[
+  {
+    "query": "climate change"
+  },
+  {
+    "query": "Ukraine conflict",
+    "timespan": "1m"
+  }
+]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "metric": {
+      "description": "Metric type: volume_pct (% of news)",
+      "type": "string"
+    },
+    "points": {
+      "description": "Number of datapoints returned",
+      "type": "number"
+    },
+    "query": {
+      "description": "The query string used",
+      "type": "string"
+    },
+    "series": {
+      "description": "Day-by-day volume datapoints",
+      "items": {
+        "properties": {
+          "date": {
+            "description": "Date of datapoint",
+            "type": "string"
+          },
+          "value": {
+            "description": "Volume as percentage of total news",
+            "type": "number"
+          }
+        },
+        "required": [
+          "date",
+          "value"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "timespan": {
+      "description": "Lookback window applied (default 1m)",
+      "type": "string"
+    }
+  },
+  "required": [
+    "query",
+    "timespan",
+    "metric",
+    "points",
+    "series"
+  ],
+  "type": "object"
+}

First observedMay 11, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds value by disclosing the output format (datapoints with timestamp and intensity as %), the daily granularity, and cost comparison ('cheaper than search_articles'), going beyond the annotations to inform the agent about behaviors.

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 three sentences, each adding substantive value: the first defines the core function, the second lists use cases, the third provides a comparison with a sibling tool. It is front-loaded and contains no filler or repetition.

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?

Given the tool's complexity (4 parameters, output schema present, rich annotations), the description is complete. It explains the output, use cases, and when alternatives are better. The presence of an output schema means return values do not need to be detailed, and the description covers the remaining aspects thoroughly.

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 coverage is 100%, so the schema already documents all four parameters. The description adds practical meaning through examples and explains the conditional usage of startdatetime/enddatetime with timespan=custom, which enhances understanding beyond the schema's descriptions.

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 defines the tool's purpose: returning the daily share of global news attention for a query as a percentage. It distinguishes itself from sibling tools like timeline_tone and search_articles by specifying what the tool returns (volume curve) and when it's appropriate to use (when only the volume curve is needed, not source articles).

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 states when to use the tool ('detect news-cycle spikes', 'benchmark attention against history', 'pair with timeline_tone') and when not to ('cheaper than search_articles when you only need the volume curve, not the source articles themselves'). This provides clear guidance on alternatives and context.

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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