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gdelt_tv_concept_entities

List GDELT Television 2.0 AI concept entities from US TV captioning, most frequent first, so you can get the correct mid codes for TV search and chart queries.

Instructions

List GDELT Television 2.0 AI concept entities. Lists the Google Knowledge Graph concept entities GDELT recognises in US television captioning, most frequent first, up to limit entries (default 100, max 10000). Each has a display label and a mid code. Pass the mid, not the label, as concept 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

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does a good job: it discloses sort order (most frequent first), the default (100) and maximum (10000) size, that no query is required, and the shape of each returned item (label + mid). It omits auth/rate-limit context and whether a smaller cap truncates silently, keeping it from a 5.

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?

Front-loads the purpose and each sentence adds information (limits, output fields, downstream usage). Slightly redundant opening where 'List GDELT Television 2.0 AI concept entities' is immediately restated as 'Lists the Google Knowledge Graph concept entities...', which could be tightened into one sentence.

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

Completeness5/5

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

For a single-parameter, no-output-schema listing tool, the description covers everything an agent needs: what is returned (label + mid), ordering, size bounds, and how to consume the mids downstream. Nothing material is missing.

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%, so a 3 is the baseline, but the description adds constraints the schema lacks — the default of 100 and the max of 10000 — plus the most-frequent-first ordering that governs the single `limit` parameter. This is real added meaning beyond the schema text.

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?

States a specific verb and resource ('List GDELT Television 2.0 AI concept entities') and defines the domain precisely ('Google Knowledge Graph concept entities GDELT recognises in US television captioning'). The wording implicitly distinguishes it from gdelt_tv_visual_entities by naming the entity class, and the downstream-use note clarifies its role among the GDELT TV siblings.

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?

Makes the usage context clear: this is a reference list whose mids are meant to be passed as `concept` to the GDELT TV search and chart endpoints, and it states 'the list does not depend on any query.' It stops short of explicitly naming which sibling to call instead or when not to use it, but the intended workflow is unambiguous.

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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