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Newsdata

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

A4.4/5.0
Behavior4/5

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

Description adds behavioral details beyond annotations: probes via ai_visibility_check, ranks by score, treats first entity as subject. Annotations already cover safety (readOnly, idempotent, non-destructive), so additional context is valuable.

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?

Three concise sentences front-loaded with purpose and key behavior. Every sentence adds value; no wasted words.

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?

Despite no output schema, description specifies return fields (score, confidence, signal density). Parameters are fully covered. Slight gap: no mention of error handling or edge cases (e.g., empty results).

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%, but description adds meaning: explains that 'entities' first entry is treated as subject for narrative, and clarifies purpose of optional fields (e.g., context disambiguates names).

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?

Description specifies the action ('compare', 'rank') and resource ('AI visibility across multiple entities'), clearly distinguishing from sibling tools like ai_visibility_check (single entity) and compare_entities (generic).

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?

Explicitly mentions use case ('competitive AI-marketing audits') and provides a concrete example question, but does not state when not to use or list explicit alternatives beyond the sibling context.

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

A4/5.0
Disambiguation3/5

Most tools have clearly distinct roles, and the extensive descriptions help differentiate intent, but the ask_pipeworx family—especially ask_pipeworx_beta, which is currently identical to ask_pipeworx—creates real ambiguity. Overlapping entry points like ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim could also cause misselection without careful reading.

Naming Consistency4/5

All 34 tools use consistent snake_case, and clear verb-led or noun-prefixed patterns emerge across families like ask_pipeworx*, polymarket_*, and remember/recall/forget. Minor deviations such as ai_visibility_check and recent_changes being noun phrases rather than verb_noun constructions prevent a perfect score.

Tool Count2/5

34 tools is well above the 25-tool threshold for over-scoping, making the set heavy for an agent to navigate. While the server spans many domains, several tools like generate_llms_txt, scan_dependency, and ai_visibility_check feel tangential to the core news/research purpose and would be better split into separate servers.

Completeness4/5

The core news/research domain is well covered: lookup, grounded verification, deep multi-source research, entity profiles, comparisons, subscriptions, and alert feeds are all present with no obvious dead ends. Minor gaps exist—such as no direct full-text article retrieval or a dedicated free-text news search beyond latest_news filters—but agents can work around them.

Resources