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events.global.timeline

Track keyword mention volume in global news coverage over time to identify trending topics and media spikes using GDELT Project data.

Instructions

Track mention volume of any topic over time — see when a keyword spikes in global news coverage. Up to 3 months of data (GDELT Project)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesQuery to track mention volume over time (e.g. "Bitcoin", "artificial intelligence")
timespanNoTime window: "1d", "7d", "30d", "3m" (default: 3 months)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It successfully discloses the 3-month data limitation and identifies the data source (GDELT Project), but fails to mention safety profile (read-only status), rate limits, authentication requirements, or the specific structure of returned timeline data.

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 consists of two efficiently structured sentences with zero waste. The first sentence front-loads the core action and value proposition (tracking volume/spikes), while the second provides essential constraints (3-month limit, data source).

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?

For a simple 2-parameter tool without output schema or annotations, the description adequately covers purpose, scope, and data constraints. It could be improved by briefly describing the return format (e.g., time-series data points) since no output schema exists to document this.

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?

With 100% schema description coverage, the schema already documents both parameters adequately. The description adds semantic context by mapping 'query' to 'keyword' and 'timespan' to the '3 months' constraint, but doesn't elaborate on parameter formats or validation rules beyond what's in the schema.

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 provides a specific verb ('Track') + resource ('mention volume') + scope ('global news coverage'), clearly distinguishing it from sibling tools like 'events.global.search' by emphasizing temporal analysis ('over time', 'spikes'). The GDELT Project citation adds specific provenance.

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

Usage Guidelines3/5

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

The description implies usage through phrases like 'over time' and 'see when a keyword spikes', suggesting trend analysis use cases. However, it lacks explicit guidance on when to choose this versus 'events.global.search' or news tools, and doesn't specify prerequisites or rate limiting concerns.

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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