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gdelt_timeline

Monitor how global news coverage volume or tone for a GDELT query changes over time. Use custom time windows, filter by domain, country, or language, and apply smoothing to reveal trends.

Instructions

Coverage volume or tone over time for a GDELT query. Return how a query's global news coverage has trended over.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd of an absolute time window. Same formats as from.
fromNoStart of an absolute time window. Accepts YYYY-MM-DD, RFC3339, or GDELT's raw YYYYMMDDHHMMSS. Cannot be combined with timespan.
queryYesGDELT query string. Same syntax as gdelt-search's query.
domainNoConvenience domain filter, appended to the query as domain:VALUE.
metricNoTimeline metric. lang and country return one series per language/country instead of one aggregate series.
smoothNoMoving-window smoothing applied to the timeline, in steps. 0 disables smoothing.
countryNoConvenience source-country filter, appended to the query as sourcecountry:VALUE.
languageNoConvenience source-language filter, appended to the query as sourcelang:VALUE.
timespanNoRelative time window ending now, e.g. 1h, 2d, 1w, 3m. Cannot be combined with from/to.
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It conveys that the output is a timeline of coverage volume or tone, but it does not explain output granularity, aggregation behavior, how metric affects series count, or what 'trended over' concretely returns. This leaves an agent uncertain about the response shape.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded, but the second sentence ('Return how a query's global news coverage has trended over.') largely restates the first sentence with awkward grammar. It is concise enough, but the second sentence does not earn its place.

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?

Given 9 parameters, no output schema, and no annotations, the description is too thin. It does not describe the time-series return structure, default time behavior, or the effect of options like metric and smooth. An agent could infer basic usage from the schema but would lack important context about what the result actually looks like.

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%, so the input schema already documents all 9 parameters well. The description adds no parameter-level meaning beyond the schema, which meets the baseline but does not elevate it.

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 ('Return') and a clear resource: coverage volume or tone over time for a GDELT query. It makes the tool's core function understandable, though it does not explicitly distinguish itself from sibling gdelt_tonechart, which also involves tone.

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?

There is no guidance on when to use this tool versus alternatives like gdelt_search, gdelt_tonechart, or gdelt_tv_timeline. The description implies a time-series use case but provides no exclusions or explicit routing cues for an agent.

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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