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mcp server

gdelt-mcp-server

Search and analyze global news coverage and US TV transcripts via the GDELT Project APIs.

Description as published by the maintainer. Source

  • version 0.2.6
  • active
  • retrieval

active — Most recent push to the repository was 2026-07-24. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.

What this server can do

9 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

gdelt_get_coverage_breakdown(query, series, timespan, breakdownBy, endDatetime, startDatetime)
Break down news coverage volume over time by source language or source country, returning a multi-series time series (one series per language or country). Shows which countries or languages drove early vs. late coverage — useful for tracing how a story propagated geographically or across language communities. Returns up to 10 series by total volume and aggregates the rest into an "Other" bucket, naming every series it folded in there under otherSeriesLabels — pass any of those labels back as the series input to get that series complete, ranked or not. Values are normalized: each point is the topic's share of media output, not an absolute article count. Small media markets with concentrated coverage therefore rank above large markets with diverse output — a high value means the topic dominated that source's coverage, not that it published the most articles. Use breakdownBy "country" with the signal-detection chain to map geographic attention, or "language" to detect non-English media surges. Required: query, breakdownBy.
gdelt_get_coverage_timeline(mode, query, points, timespan, smoothing, endDatetime, startDatetime)
Retrieve a time series showing when news coverage of a topic spiked, or how average tone shifted over time. Use mode "volume" for normalized coverage intensity (% of all global coverage per timestep). Use mode "volume_with_articles" for the same signal plus the top articles that drove each spike — this is the primary signal-detection mode: a single call reveals both the spike and its cause, avoiding a follow-up gdelt_search_articles call. Use mode "tone" for average sentiment score per timestep (negative = hostile/fearful, positive = celebratory). Date resolution is automatically chosen based on timespan: hours for short windows, days for longer ones. In volume_with_articles mode the text surface shows the first 3 article links per timestep next to that timestep's true article count; name a timestep's date in points to render its full list. Note: DOC API covers only the last 3 months. Required: query.
gdelt_get_tone_distribution(query, timespan, endDatetime, startDatetime)
Get the tonal distribution of articles matching a query as a histogram (bins approximately -30 to +30). Unlike a single average tone score, the histogram reveals whether coverage is uniformly negative, bimodal (some articles extremely positive and some extremely negative), or clustered near neutral. Each bin includes representative article URLs. Distinct from gdelt_get_coverage_timeline (mode: tone) — this is a snapshot distribution across all matching articles, not a time series. Use gdelt_get_coverage_timeline with mode "tone" to see how sentiment shifted over time. Required: query.
gdelt_get_tv_clips(sort, query, stations, timespan, maxRecords, endDatetime, startDatetime)
Retrieve the top matching TV news clips (up to 3,000) for a query from the Internet Archive's Television News Archive. Each clip includes show name, station, air timestamp, a 15-second transcript excerpt, and a direct link to view the full one-minute clip. Use after gdelt_search_tv to read the actual transcript content driving a coverage spike. 3,000 is a hard per-call ceiling and GDELT offers no cursor: when a query fills it, split the run into narrower startDatetime/endDatetime windows — the response hands back the exact windows to use. Archive coverage spans 2009–October 2024. Required: query.
gdelt_get_tv_context(query, stations, timespan, endDatetime, startDatetime)
Get the top co-occurring words and phrases from TV news clips matching a query — the vocabulary framing a topic on television. Returns the most frequent non-stopword terms from matching clips, with relative frequency scores (0–100, where 100 = the query term itself). Use to understand narrative framing, identify related concepts mentioned alongside a topic, or generate follow-up search terms. TV data spans 2009–October 2024. Required: query.
gdelt_get_tv_trending
Retrieve trending topics, keywords, and phrases currently dominating US television news across national networks. No query required — returns the top memes of the present news cycle. Updated every 15 minutes. Note: the GDELT TV archive feed stopped updating around October 2024; results from this endpoint reflect that most-recent archived data rather than a live feed.
gdelt_list_tv_stations
List all television stations available for TV search with their market, network, monitoring start date, and monitoring end date. Stations with an end date within the last 24 hours are flagged as active; stations with earlier end dates are discontinued. Use before querying to verify a station was active during the target time period, or to discover valid station IDs for the stations parameter in other TV tools. Most station monitoring ended October 2024 when the Internet Archive TV feed stopped updating.
gdelt_search_articles(sort, query, timespan, maxRecords, endDatetime, startDatetime)
Search the last 3 months of global news coverage (65+ languages) using the GDELT DOC API. Returns up to 250 articles with URL, title, source domain, language, country, publication date, and social image URL. Query supports full GDELT syntax: phrases ("bird flu"), boolean OR ((flu OR pandemic)), source country (sourcecountry:china), source language (sourcelang:spanish), domain (domain:who.int), GKG theme (theme:DISEASE_OUTBREAK), tone filter (tone<-5 for negative), proximity (near20:"flu virus"), and repeat (repeat3:"outbreak"). 250 is a hard per-call ceiling and GDELT offers no cursor: when a query fills it, split the run into narrower startDatetime/endDatetime windows — the response hands back the exact windows to use. Note: this API covers only the most recent 3 months — use gdelt_search_tv for historical TV transcripts back to 2009. Required: query.
gdelt_search_tv(query, stations, timespan, normalize, smoothing, endDatetime, startDatetime)
Search US television news closed captions (2009–October 2024, 150+ stations) for spoken mentions of a query. Returns a normalized per-station time series showing relative airtime devoted to the topic. Use the stations parameter to select networks (e.g. ["CNN", "FOXNEWS", "MSNBC"]) — the TV API requires at least one station, supplied either there or as a station: selector inside query. TV query also supports in-query operators: station:CNN, network:CBS, market:"National", show:"Anderson Cooper 360", context:"vaccine". Important: most station monitoring ended October 2024 — use gdelt_list_tv_stations to verify active date ranges before querying recent events. Required: query.

Last successful function declaration observed on . Source: https://gdelt.caseyjhand.com/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on . Source: https://gdelt.caseyjhand.com/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Signal Value What it measures Window Observed Source
GitHub stars 3 Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. cumulative, all time GitHub
Last commit 2026-07-24 Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. point in time GitHub
Open issues 3 Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. as of fetch GitHub
Latest published version 0.2.6 Latest version string the maintainer published to the registry. as of fetch Model Context Protocol
Registry record last updated 2026-07-24 When the registry record was last updated by its maintainer. point in time Model Context Protocol
License Apache-2.0 Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. as of fetch GitHub
First listed in the MCP Registry 2026-07-24 Date this server was first published to the official MCP Registry. Not a usage or quality measure. point in time Model Context Protocol
repository status active The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. as of fetch GitHub
mcp tools declared 9 tools Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. as of probe gdelt.caseyjhand.com
mcp endpoint status ok The server listed 9 functions when asked. as of probe gdelt.caseyjhand.com

Where to get it

Related, by what their authors tagged them

  • News Sentiment API — last commit 2026-08-03, shares media-monitoring, sentiment-analysis
    News sentiment score trends for any topic over time. Free key at trendsapi.ai
  • io.github.cyanheads/guardian-mcp-server — last commit 2026-07-30, shares journalism
    Guardian Open Platform — search and retrieve full article text from The Guardian archive.
  • GDELT Global Events — last commit 2026-06-11, shares gdelt
    Geopolitical event detection, tone timeseries, actor trends from GDELT 2.0.
  • News Volume API — last commit 2026-08-03, shares media-monitoring
    News article mention volume trends for any topic. Free key at trendsapi.ai
  • Sentiment Analyzer API — last commit 2026-07-19, shares sentiment-analysis
    Analyze text sentiment, emotions, confidence scores, and key phrases. x402 USDC.
  • io.github.Etha0916/praesentire-mcp — last commit 2026-06-12, shares sentiment-analysis
    Bilingual financial news sentiment (English + Traditional Chinese) scored by Claude.
  • Social Trends Intelligence MCP — last commit 2026-07-16, shares sentiment-analysis
    Trending topics, cross-platform sentiment, viral content, community pulse & brand mentions.
  • io.github.cyanheads/anime-mcp-server — last commit 2026-07-30, shares cyanheads
    Search anime/manga, franchise watch order, schedule, characters, rankings, studio filmography.
  • io.github.cyanheads/arxiv-mcp-server — last commit 2026-07-27, shares cyanheads
    Search arXiv, fetch paper metadata, and read full-text content.
  • io.github.cyanheads/astronomy-mcp-server — last commit 2026-07-31, shares cyanheads
    Offline observational astronomy: positions, rise/set, moon phases, eclipses, and seasons.

These share tags the maintainers applied themselves, such as media-monitoring, sentiment-analysis, journalism, gdelt. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.

This is not a recommendation and not a test result. It is a map of what the authors said their work is about.

Also from cyanheads

How the author describes it

Topics the maintainer set on GitHub: ai-agents, ai-tools, cyanheads, event-monitoring, gdelt, global-news, journalism, mcp, mcp-server, media-analysis, media-monitoring, model-context-protocol, news-analytics, sentiment-analysis, tv-news, typescript.

This record as data

Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.

GET /api/v1/entries/mcp_server.json

Sources

  1. cyanheads/gdelt-mcp-server on GitHub — GitHub, observed , trust tier 3.
  2. Tools declared by the MCP server at https://gdelt.caseyjhand.com/mcp — gdelt.caseyjhand.com, observed , trust tier 4.
  3. Official MCP Registry — Model Context Protocol, observed , trust tier 1.