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gdelt_tv_visual_entities

Lists computer-vision object and activity labels GDELT recognizes in US television video by frequency, yielding values for the visual parameter in GDELT TV search and chart queries.

Instructions

List GDELT Television 2.0 AI visual entities. Lists the computer-vision object and activity labels GDELT recognises in US television video, most frequent first, up to limit entries (default 100, max 10000). The label itself is the value to pass as visual to the GDELT TV search and chart endpoints. The list does not depend on any query.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum entries to return, most-frequent first.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden well: it discloses ordering (most frequent first), the default (100) and max (10000) of the limit, and that no query is required. It doesn't mention auth or rate limits, but for a simple read-only enumeration this is solid disclosure.

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

Conciseness4/5

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

Four short sentences, front-loaded with the identity then the practical usage. Sentence 2 somewhat re-states sentence 1 ('lists ... labels'), a minor redundancy, but nothing else is wasted.

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 single-optional-parameter enumeration tool with no output schema, the description covers what is returned (labels, ordered by frequency), the cap, and how to consume the values downstream. An agent has everything it needs to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the schema already describes `limit`. The description adds concrete constraint values (default 100, max 10000) that the schema omits, which is genuine added meaning over the structured field.

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 gives a specific verb and resource (list GDELT Television 2.0 AI visual entities) and defines what those entities are: computer-vision object and activity labels from US television video. It implicitly separates itself from the 'concept entities' sibling by specifying visual/computer-vision labels, but never names that sibling explicitly.

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

Usage Guidelines4/5

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

It clearly states the intended use: the returned label is the value to pass as `visual` to the GDELT TV search and chart endpoints, and notes the list is query-independent. This tells the agent when to reach for it as an enumeration source, but offers no explicit alternatives or when-not guidance versus the other gdelt_tv_* tools.

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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