Gdelt

Timeline Tone

timeline_tone

Read-onlyIdempotent

Day-by-day AVERAGE NEWS SENTIMENT for a GDELT query over time. Returns datapoints with timestamp + tone value (-100 very negative .. +100 very positive, computed from GDELT's sentiment scoring of every article matching the query). Use for tracking sentiment shifts around a topic, person, country, or event ("how did press coverage of X change after Y happened"). Pair with timeline_volume to chart sentiment vs interest — interest spike + sentiment drop = something bad just happened.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "Bitcoin sourcelang:eng"
    },
    "name": "timeline_tone"
  }
}

Input Schema

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

Output Schema

Name	Required	Description
`query`	Yes	The query string used
`metric`	Yes	Metric type: avg_tone (-100..+100)
`points`	Yes	Number of datapoints returned
`series`	Yes	Day-by-day tone 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": "Bitcoin sourcelang:eng"
+  },
+  {
+    "enddatetime": "20240101000000",
+    "query": "Trump sourcecountry:US",
+    "startdatetime": "20231201000000",
+    "timespan": "custom"
+  }
+]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "metric": {
+      "description": "Metric type: avg_tone (-100..+100)",
+      "type": "string"
+    },
+    "points": {
+      "description": "Number of datapoints returned",
+      "type": "number"
+    },
+    "query": {
+      "description": "The query string used",
+      "type": "string"
+    },
+    "series": {
+      "description": "Day-by-day tone datapoints",
+      "items": {
+        "properties": {
+          "date": {
+            "description": "Date of datapoint",
+            "type": "string"
+          },
+          "value": {
+            "description": "Average tone value for the day",
+            "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

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds detail: 'Returns datapoints with timestamp + tone value (-100 very negative .. +100 very positive, computed from GDELT's sentiment scoring of every article matching the query).' This explains the output format and derivation. No mention of pagination or rate limits, but sufficient.

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 plus a usage note. Front-loaded with core functionality. No filler words. Each sentence adds value: first defines the tool, second explains return and usage example, third pairs with sibling.

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 has an output schema (not shown but indicated), the description explains return values (timestamp + tone value range). Covers usage context and pairing. Missing details on error handling or edge cases, but overall complete for a read-only query tool with good annotations.

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?

Schema description coverage is 100% (all parameters have descriptions). Description does not add much parameter-level detail beyond schema, but explains the tone value's meaning and range. Baseline 3 is appropriate as schema does the heavy lifting.

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?

Clearly states 'Day-by-day AVERAGE NEWS SENTIMENT for a GDELT query over time.' Specifies the verb (returns average sentiment) and resource (GDELT query over time). Differentiates from sibling timeline_volume by mentioning pairing for sentiment vs interest analysis.

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?

Explicitly says 'Use for tracking sentiment shifts around a topic, person, country, or event.' Provides a concrete scenario: 'Pair with timeline_volume to chart sentiment vs interest — interest spike + sentiment drop = something bad just happened.' This gives clear guidance on when to use and how to combine with alternatives.

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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