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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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds that it internally probes using 'ai_visibility_check' and returns a ranked list with score, confidence, signal density. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences cover function, use case, and output. Front-loaded with main action. No unnecessary words, though could be slightly more compact.

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?

Given no output schema, the description explains output fields (score, confidence, signal density) and internal call to ai_visibility_check. Input parameters are well-covered. Minor gap: exact ranking logic not detailed, but sufficient.

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%. The description adds context: entities array 2-8 items, first is subject rest competitors; models default is workers-ai; context disambiguates names. Adds value beyond 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 clearly states the tool compares AI visibility across multiple entities side-by-side, ranks them, and surfaces most/least recognized. It distinguishes from sibling tool 'ai_visibility_check' by focusing on multiple entities.

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 use case example and notes the first entity is treated as the subject. It implies use for competitive audits, but could explicitly mention when not to use it (e.g., single entity check) compared to siblings.

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
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions differentiating similar tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between ask_pipeworx and ask_pipeworx_beta, as both serve as universal routers with only minor routing improvements.

Naming Consistency2/5

Tool names follow inconsistent patterns: some use snake_case (ask_pipeworx, entity_profile), others use lowercase single words (forget, recall), and some use camelCase (bet_research, deep_research). This mix of conventions makes the naming scheme unpredictable.

Tool Count3/5

With 35 tools, the server covers a wide range of data sources, but the count feels slightly heavy for the apparent scope. Several tools serve meta-purposes (discover_tools, suggest_questions) or specialized functions (polymarket_arbitrage), adding to the complexity.

Completeness4/5

The tool set offers comprehensive coverage for financial, economic, drug, and news data, including comparison and grounding capabilities. However, the football-related tools are limited to German leagues, leaving a minor gap for other sports or regions.

Resources