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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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds that the tool probes each entity with 'ai_visibility_check', ranks by score, and returns a ranked list with score, confidence, and signal density. This complements the 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?

The description is three sentences with no filler. It front-loads the core action ('Compare AI visibility across multiple entities'), includes a concrete use case, and efficiently covers output format. Every sentence adds value.

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 no output schema, the description fully compensates by stating the return format (ranked list with score, confidence, signal density). It covers purpose, input parameters, and usage context thoroughly. For a tool with 4 parameters and no output schema, this is complete.

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 cover all 4 parameters (100% coverage). The description adds value by explaining that 'context' applies to every probe, that 'entities' first entry is the subject, and that 'models' defaults to workers-ai. This goes beyond the schema's basic 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 uses specific verbs ('compare', 'probes', 'ranks') and identifies the resource ('AI visibility across multiple entities'). It distinguishes this tool from siblings like 'ai_visibility_check' by emphasizing side-by-side comparison and ranking, and provides a concrete competitive audit example.

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 explicitly states the tool is 'useful for competitive AI-marketing audits' and gives a sample question. It implies when to use (multi-entity comparison) and the first entity as subject. It doesn't explicitly state when not to use, but the context is clear enough.

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

Multiple tools answer factual questions (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, validate_claim, deep_research), and ask_pipeworx_beta is explicitly identical to the stable router right now, creating genuine selection ambiguity. Most other clusters—memory, subscriptions, entity research, Polymarket—are reasonably distinct once the verbose descriptions are read.

Naming Consistency4/5

Names are consistently lowercase snake_case with recognizable family prefixes (ask_pipeworx_*, data360_*, polymarket_*, pipeworx_*), which aids grouping. The convention mixes verb-first names like resolve_entity with noun/prefix names like polymarket_edges, but it is still readable and predictable enough.

Tool Count2/5

34 tools is well past the heavy range, and the server bundles several unrelated concerns—universal data routing, prediction-market analytics, memory, subscriptions, AI-visibility marketing, and npm dependency scanning—into one surface. Many tools earn their place, but the aggregate is overloaded and likely to slow tool-selection.

Completeness4/5

The data-research side has strong coverage: discovery, universal routing, grounded verification, entity resolution, profiles, comparisons, recent-changes tracking, and in-record search. Subscription lifecycle and memory are complete, and the prediction-market suite even covers fill-risk and edge persistence; only a few niche read/write operations are absent.

Resources