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Gdelt

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already flag readOnlyHint, idempotentHint, and openWorldHint, so safety and idempotency are known. The description adds that the tool probes each entity with ai_visibility_check and returns a ranked list, but provides no additional behavioral caveats (e.g., rate limits, data freshness).

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 two sentences plus a brief example, front-loading the core purpose. Every sentence adds value; no redundancy or fluff.

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?

With 4 parameters fully described in schema and a clear output format hinted in the description, the tool definition is complete for an agent to correctly select and invoke it. No missing context.

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 description coverage is 100%, so parameters are already documented. The description adds value by specifying that the first entity in the 'entities' array is treated as the 'subject' for narrative, and mentions default model behavior, which goes beyond 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 explicitly states the tool compares AI visibility across multiple entities side-by-side, probes with ai_visibility_check, ranks by score, and surfaces recognition levels. This clearly distinguishes it from single-entity tools like ai_visibility_check.

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?

The description provides a concrete use case ('competitive AI-marketing audits') with an example query, implying when to use. It does not explicitly call out alternatives or when not to use, but the context is clear.

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.

Resources