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gdelt_tv_wordcloud

Generate a frequency-ranked word cloud from US television news coverage for a specific station and channel. Filter by time, show, transcripts, captions, or visual labels to visualize key terms.

Instructions

Word cloud for US television news coverage. Return a frequency-ranked word/label cloud for one match.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	End of an absolute time window. Same formats as from.
`from`	No	Start of an absolute time window. Accepts YYYY-MM-DD, RFC3339, or GDELT's raw YYYYMMDDHHMMSS. Cannot be combined with timespan.
`show`	No	Limit to an exact show name.
`visual`	No	Search visual object/activity labels from computer vision (GDELT's visual: operator). Repeatable; multiple values are OR'd together.
`caption`	No	Search human-provided closed captioning (GDELT's cap: operator). Repeatable; multiple values are OR'd together.
`channel`	Yes	Required. Which match channel to build a word cloud from.
`concept`	No	Search Google Knowledge Graph concepts extracted from captioning, by MID code (GDELT's capnlp: operator). Repeatable; multiple values are OR'd together.
`station`	Yes	Required. Station to search.
`timespan`	No	Relative time window ending now, e.g. 1h, 7d, 3m, 1y. Cannot be combined with from/to. GDELT's TV archive starts July 6, 2010.
`transcript`	No	Search machine-generated speech-to-text transcripts (GDELT's asr: operator). Repeatable; multiple values are OR'd together. Short phrases only (GDELT caps each at 5 words).
`day_of_week`	No	Limit to a day of week, 0 (Sunday) through 7 (Saturday), PST.
`onscreen_text`	No	Search OCR'd onscreen text/chyrons (GDELT's ocr: operator). Repeatable; multiple values are OR'd together. Short phrases only (GDELT caps each at 5 words).
`exclude_visual`	No	Exclude clips whose visual labels match this value (GDELT's -visual: operator). Repeatable; every value must be absent.
`exclude_caption`	No	Exclude clips whose closed captioning matches this value (GDELT's -cap: operator). Repeatable; every value must be absent.
`exclude_concept`	No	Exclude clips whose extracted concepts match this MID code (GDELT's -capnlp: operator). Repeatable; every value must be absent.
`exclude_transcript`	No	Exclude clips whose speech-to-text transcript matches this value (GDELT's -asr: operator). Repeatable; every value must be absent.
`exclude_onscreen_text`	No	Exclude clips whose OCR'd onscreen text matches this value (GDELT's -ocr: operator). Repeatable; every value must be absent.

Schema Changelog

Changes observed during successful MCP inspections.

Changed12 schema fields changedv1.17.5Sep 26, 2026
- addedInput schema / properties / caption / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / channel / enum
```
Added value: +[
+  "transcript",
+  "caption",
+  "concept",
+  "onscreen_text",
+  "visual"
+]
```
- addedInput schema / properties / concept / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / exclude_caption / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / exclude_concept / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / exclude_onscreen_text / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / exclude_transcript / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / exclude_visual / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / onscreen_text / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / station / enum
```
Added value: +[
+  "CNN",
+  "MSNBC",
+  "FOXNEWS",
+  "BBCNEWS",
+  "KGO",
+  "KPIX",
+  "KNTV"
+]
```
- addedInput schema / properties / transcript / items
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / visual / items
```
Added value: +{
+  "type": "string"
+}
```
Addedv1.16.0Aug 29, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the output is frequency-ranked and scoped to 'one match', which adds some behavioral context. However, it does not explain what 'one match' means, how filters combine, or the exact structure of the returned cloud, leaving notable gaps for a 17-parameter read tool.

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 short sentences that are front-loaded with the core purpose and output type. Every word earns its place; there is no filler or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a complex 17-parameter tool with no output schema and only a two-sentence description. The central concept of 'one match' is left undefined, and there is no explanation of how the many filter parameters relate to each other or what the final response contains beyond a vague 'word/label cloud'.

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%, with all 17 parameters individually documented, so the baseline is 3. The description itself adds no parameter-level meaning beyond the schema, and the vague 'one match' phrase does not clarify any specific parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Return a frequency-ranked word/label cloud' for 'US television news coverage', which clearly conveys what the tool produces. However, it does not distinguish itself from sibling GDELT tools like gdelt_tv_search or gdelt_tv_timeline, and the phrase 'for one match' is ambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternative GDELT tools. It lacks any 'use when' context, exclusions, or references to siblings, leaving the agent to infer the appropriate scenario from the tool name alone.

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

Deploy Server

gdelt_tv_wordcloud

Instructions

Input Schema

Schema Changelog

TDQS

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