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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

A4.7/5.0
Behavior5/5

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

Explains internal mechanism (probes each entity with ai_visibility_check, ranks by score) and output format (ranked list with score, confidence, signal density per entity). Adds value beyond annotations which only indicate readOnly, openWorld, idempotent, non-destructive.

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, no fluff. Main purpose is front-loaded. Each sentence provides essential information without redundancy.

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?

Given no output schema, description specifies return structure. Covers role of each parameter, internal operation, and output format. Sufficient for agent to use correctly.

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% so parameters are already documented. Description adds contextual info about the 'entities' parameter (first entry treated as subject). No repetition, but adds meaning 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?

Clearly states 'Compare AI visibility across multiple entities side-by-side.' Identifies specific verb and resource, and distinguishes from sibling ai_visibility_check which is for single entities. Includes an example use case.

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?

Describes use case ('competitive AI-marketing audits') and gives an example question. Notes that first entity is treated as subject. However, does not explicitly mention when not to use or contrast with ai_visibility_check.

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

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, entity_profile, compare_entities, and validate_claim, all retrieving entity data. Agents may struggle to choose correctly between them. Likewise, bet_research, polymarket_edges, and polymarket_arbitrage cover similar prediction market territory.

Naming Consistency3/5

All tool names use underscores, but the verb/noun order varies: compare_entities (verb_noun), entity_profile (noun_noun), scan_competitor_ai_presence (verb_noun), bet_research (noun_verb). Some names are overly long (scan_competitor_ai_presence). The pattern is readable but not fully consistent.

Tool Count3/5

With 31 tools, the count is high but not extreme. However, the set covers geospatial, data retrieval, prediction markets, npm scanning, and memory/feedback - too broad for a single server. Many tools feel added without clear justification, making the set feel bloated.

Completeness2/5

The geospatial subset is incomplete (missing elevation, distance matrix, isochrones). The data access tools overlap rather than form a complete API surface (e.g., no entity update/delete). Prediction market tools are numerous but redundant. The server tries to do too much and lacks depth in any area.

Resources