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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, openWorldHint, idempotentHint, and non-destructive; the description adds that the tool probes each entity with ai_visibility_check and ranks results, and specifies output fields. This adds behavioral context beyond the annotations without contradicting them.

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, each contributing: purpose, mechanism (probe with ai_visibility_check and rank), and use case/results. No filler or redundancy; information is front-loaded.

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 adequately describes the return format (ranked list with score, confidence, signal density). It also covers the tool's purpose, process, and a representative use case. The schema handles parameters, and annotations cover safety, so the description is complete for this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers all 4 parameters with descriptions (100% coverage), so the description adds no new parameter meaning. It references entities as 'your brand + N competitors' which mirrors the schema's explanation of first entry as subject. Baseline 3 applies.

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 a specific action ('Compare AI visibility across multiple entities side-by-side'), specifies the resource (entities' AI presence), and differentiates from sibling tools like ai_visibility_check by emphasizing side-by-side comparison and ranking. It includes concrete output details (ranked list with score/confidence/signal density), making the purpose unmistakable.

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 clear use case ('competitive AI-marketing audits') and an example question. It implicitly contrasts with single-entity checks via ai_visibility_check, but does not explicitly state when not to use this tool or name alternatives like compare_entities. Thus it has clear context but no exclusions.

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

A3.7/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying catalog, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all target prediction-market opportunities. entity_profile, recent_changes, and compare_entities also share company-research territory, making misselection likely without reading long descriptions carefully.

Naming Consistency3/5

All names use snake_case, but conventions are mixed: some are verb_noun (list_subscriptions, generate_llms_txt, resolve_entity), some are noun phrases (entity_profile, rba_cash_rate), and some are brand-prefixed product names (ask_pipeworx, pipeworx_trending). The polymarket_* and rba_* families are internally consistent, but the overall surface has no single predictable pattern.

Tool Count2/5

35 tools is a large surface, well above the 25+ threshold that typically becomes unwieldy. While the server covers a broad domain (data lookup, prediction markets, memory, subscriptions, company research), many tools are niche variants (ask_pipeworx_beta, polymarket_edge_tracker, scan_competitor_ai_presence) that add cognitive load rather than earning their place.

Completeness3/5

Core flows are well covered: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and data access has ask_pipeworx plus grounded and research variants. However, the surface is sprawly and uneven — prediction markets get six tools while other domain areas rely on generic routing, and the server's overall purpose is diffuse enough that gaps are hard to assess cleanly.

Resources