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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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds valuable context: it probes each entity with ai_visibility_check, ranks results, and returns per-entity score, confidence, and signal density. No contradiction with annotations.

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 sentences, front-loaded with the main action. Every sentence adds value: action, mechanism, use case, output. No redundant or filler content.

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?

For a tool with 4 parameters, 100% schema coverage, no output schema, and annotations covering safety, the description adequately explains behavior and output structure (ranked list with metrics). It lacks details on pagination or error handling, but given the tool's simplicity, it is sufficiently 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 coverage is 100% with descriptions. The description adds meaning beyond schema by explaining that the first entity is treated as the 'subject' for narrative and that context disambiguates common names. This helps the agent understand parameter intent.

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 a specific verb ('Compare') and resource ('AI visibility across multiple entities'), clearly distinguishing it from sibling tools like ai_visibility_check (single entity) and compare_entities (likely different comparison method). It details the process of probing, ranking, and surfacing recognition scores.

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?

States explicit use case ('competitive AI-marketing audits') and provides an example question. Implies when to use this tool over ai_visibility_check for multi-entity comparison, but lacks explicit 'when not to use' or alternative tool mentions beyond the sibling list.

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

A3.9/5.0
Disambiguation2/5

The ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research cluster is highly overlapping—beta is explicitly identical right now and grounded differs mainly in answer extraction. Several other pairs (ai_visibility_check vs scan_competitor_ai_presence, and the six prediction-market tools) also blur boundaries, making misselection likely.

Naming Consistency3/5

Names are uniformly snake_case and benefit from clear prefixes (ask_pipeworx_, kcmo_, polymarket_, pipeworx_). However, conventions are mixed between bare verbs (forget, recall, remember), noun phrases (entity_profile, polymarket_edges, recent_alerts), and verb_noun forms, and similar names like polymarket_edges vs polymarket_edge_tracker add confusion.

Tool Count2/5

34 tools is well into the bloat range, and only 3 are actually Kansas City-specific despite the server name. The surface bundles prediction markets, memory, feedback, llms.txt generation, and npm scanning alongside data lookup, making it heavy and unfocused; several meta-tools could be collapsed.

Completeness4/5

As a read-only data research platform, the surface is quite complete: discovery, querying, grounded answers, entity resolution, profiles, comparisons, claim verification, subscriptions, and memory are all covered with few dead ends. Minor gaps exist—no subscription update, no direct citation-URI fetch tool, and a thin KC-specific set—but agents can work around them.

Resources