gdelt-mcp-server

Search GDELT GKG Themes

gdelt_search_themes

Read-only

Find GDELT Global Knowledge Graph (GKG) theme identifiers for the theme: operator that gdelt_search_articles, gdelt_get_coverage_timeline, gdelt_get_tone_distribution, and gdelt_get_coverage_breakdown accept in query. Searches the identifiers in the GDELT GKG theme lookup, which carries no labels or descriptions: every query word must begin one of the _-separated parts of an identifier or run across consecutive parts, or all the words joined must, so "drought" finds NATURAL_DISASTER_DROUGHT, "cyberattack" finds CYBER_ATTACK, "plant disease" finds TAX_PLANTDISEASE, and "wb water" narrows to World Bank water themes. There is no stemming or synonym matching — "displacement" does not reach DISPLACED — except one fallback: when nothing matches, the search retries once with a trailing s dropped from each word of four or more letters, and says so. Matches rank an exact identifier first, then by the count the lookup lists — a static prevalence figure, not a live article total — and each carries a paste-ready operator such as theme:TAX_DISEASE_OUTBREAK. Page with offset and limit.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum matches returned in this response (1–100).
`query`	Yes	Words to find in theme identifiers (e.g. "drought", "cyber attack", "refugee"), a family prefix with a word ("wb water", "crisislex"), or a whole identifier to confirm it is listed ("TAX_DISEASE_OUTBREAK"). Case-insensitive; a leading theme: is ignored; every word must match, or all the words joined must ("plant disease" matches TAX_PLANTDISEASE). Must contain at least one letter or digit.
`offset`	No	Zero-based offset into the ranked matches. Use nextOffset from the preceding response with the same query to retrieve the next page.

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No	Present when the call failed. Absent on success.
`limit`	No	Maximum matches requested for this page.
`notice`	No	Search outcome: that the plural fallback supplied the matches and which words it tried, or, when nothing matched, how to retry. Absent when the query as given matched.
`offset`	No	Zero-based offset of this page.
`matches`	No	Matches on this page: an exact identifier match first, then by count descending, then by identifier.
`nextOffset`	No	Offset for the next page with the same query. Absent when this is the final page.
`totalCount`	No	Total themes matching the query — the same figure as totalMatches.
`totalMatches`	No	Total themes matching the query, across all pages.
`effectiveQuery`	No	Echoed query string for use in follow-up calls.

Schema Changelog

Changes observed during successful MCP inspections.

AddedSep 24, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, and the description adds substantial behavioral detail beyond that: exact matching rules, the no-stemming/synonym limitation, the trailing-s retry fallback, ranking logic (exact identifier first, then static prevalence count), the paste-ready operator format, and pagination via offset/limit. It also discloses that the lookup carries no labels or descriptions and that the count is static, not live. No contradictions.

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 is dense and every sentence carries essential information. It is front-loaded with purpose and consumers, then logically progresses through matching rules, fallback, ranking, operator format, and pagination. No filler or repetition; appropriate length given the complexity.

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 tool with intricate matching logic and an existing output schema, the description covers all operational aspects an agent needs: query formulation, edge cases, fallback, ranking, and pagination. The presence of an output schema means return-value details are handled elsewhere, so the description is complete for correct invocation and response interpretation.

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

Parameters5/5

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

Despite 100% schema description coverage, the description enriches all parameters. It explains query semantics with concrete examples (case-insensitivity, leading 'theme:' ignored, word joining, family prefixes), clarifies that limit bounds the response, and that offset works with the return metadata. It goes well beyond the schema's parameter descriptions.

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 states a precise verb and resource: it finds GDELT GKG theme identifiers for the theme: operator. It explicitly names the sibling tools that accept these identifiers, distinguishing its role as a lookup helper rather than a search or analysis tool. The purpose is unmistakable and well-differentiated.

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

Usage Guidelines5/5

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

It clearly explains when to use it: to obtain theme: identifiers for the listed sibling tools. It also gives detailed query construction rules, matching semantics, and the fallback behavior, so an agent knows exactly how to invoke it and interpret results. It implicitly covers when not to use it (when you need article text, timeline data, etc.) by naming the consumers.

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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