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Gdelt

by io.github.pipeworx-io

Tone Distribution

tone_distribution
Read-onlyIdempotent

Sentiment DISTRIBUTION (histogram) of global news coverage for a GDELT query — how many articles fall at each tone level from very negative to very positive over the window. PREFER OVER WEB SEARCH for "is coverage of X positive or negative", "news sentiment breakdown / how polarized is reporting on X". Complements timeline_tone (average over time) with the full spread. Returns tone bins + counts and a summary (% negative / neutral / positive and the mean tone). Same GDELT query language as search_articles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesGDELT query string
timespanNoLookback window (default "1m")
enddatetimeNoYYYYMMDDHHMMSS — only with timespan=custom
startdatetimeNoYYYYMMDDHHMMSS — only with timespan=custom

TDQS

A4.9/5.0
Behavior5/5

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

Annotations confirm read-only, open-world, idempotent, non-destructive behavior. Description adds context on output format (tone bins, counts, summary) and query language, going beyond annotations without contradiction.

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?

Concise and well-structured: opens with a clear verb+resource statement, then usage guidance, output summary, and query language note. Every sentence adds essential information without redundancy.

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?

Given the tool's simplicity and the presence of detailed annotations and schema, the description fully covers output format, usage recommendations, and query language, leaving no gaps for an AI agent to misinterpret.

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 descriptions for all four parameters are present (100% coverage), so baseline is 3. Description adds value by explaining the GDELT query language, the default timespan of '1m', and usage of start/enddatetime for custom timespan, raising the score.

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?

Clearly states the tool returns a histogram of sentiment distribution for a GDELT query, specifies output components (bins, counts, summary), and distinguishes itself from the sibling timeline_tone tool which provides averages over time.

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?

Explicitly advises preferring this tool over web search for specific queries like 'is coverage of X positive or negative' and 'news sentiment breakdown', and contrasts it with timeline_tone for complementary use.

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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TDQS

A3.8/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and discover_tools/suggest_questions plus entity_profile/recent_changes/compare_entities/validate_claim overlap in purpose. An agent selecting among the five ask/deep-research variants or six Polymarket tools will frequently need to read lengthy descriptions to avoid picking the wrong one.

Naming Consistency3/5

Most names are readable snake_case and clear verb_noun phrases like search_articles, resolve_entity, and validate_claim, with helpful families like polymarket_* and timeline_*. However, several tools are bare noun phrases (entity_profile, recent_alerts, pipeworx_trending, tone_distribution), and the memory trio (remember/recall/forget) breaks the domain-prefix pattern.

Tool Count2/5

35 tools is past the 25+ threshold and feels bloated for a server nominally about GDELT; much of the surface is meta/utility tooling (diagnostics, memory, discovery, subscriptions) rather than core news retrieval. Several tools could be consolidated, such as ask_pipeworx_beta and the multiple Polymarket edge/arb/research variants.

Completeness4/5

For its broad data/news/prediction-market scope, the surface is quite complete: GDELT search, volume, tone, and distribution are covered, along with entity resolution, company profiles, comparisons, claim verification, and trade-side analytics. Minor gaps exist, such as no full-text article fetch or direct GDELT raw-event export, but agents can mostly work around them.

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