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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
Behavior5/5

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

Annotations declare readOnlyHint, idempotentHint, etc. The description adds transparency by revealing that it internally probes each entity with ai_visibility_check and returns a ranked list with score, confidence, and signal density. No contradictions 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?

The description is four sentences, with the primary action in the first sentence. It is efficient and contains no redundant information.

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?

Despite lacking an output schema, the description specifies the return format (ranked list with score, confidence, signal density). It provides sufficient context for the tool's complexity. One minor gap: it doesn't mention that the output includes the model names used, but overall complete enough.

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?

Schema coverage is 100% with descriptions for all four parameters. The description adds only the hint that the first entity is treated as the 'subject' for narrative. This meets the baseline expectation but does not significantly enhance understanding beyond the 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, probes each using ai_visibility_check, and ranks them. It distinguishes from the sibling ai_visibility_check (single entity) and compare_entities (different comparison). The mention of competitive AI-marketing audits provides a concrete 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?

The description explains when to use the tool (competitive audits comparing multiple brands) and implies that for a single entity one would use ai_visibility_check. It does not explicitly state when not to use or list alternatives, 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

A3.9/5.0
Disambiguation2/5

Multiple query entry points have overlapping boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical, suggest_questions and discover_tools both serve discovery/onboarding, and validate_claim overlaps with ask_pipeworx_grounded. With 34 tools including five Polymarket edge/scanner tools, an agent can easily select the wrong meta-tool despite the detailed descriptions.

Naming Consistency3/5

All names are lowercase snake_case, so there is no style chaos, but the pattern is inconsistent: verb-led names like ask_pipeworx and validate_claim mix with noun-led names like entity_profile, recent_alerts, and polymarket_arbitrage, plus bare memory verbs like remember/recall/forget. Related tools are also not aligned, such as ai_visibility_check vs scan_competitor_ai_presence.

Tool Count2/5

34 tools is too many for a server branded 'Data Toronto', and many tools are only loosely related to the core data-access purpose: ask_pipeworx_beta, generate_llms_txt, scan_dependency, ai_visibility_check, and the memory trio feel like bolt-ons. Even granting Pipeworx's broad research scope, the set is over-stuffed rather than well-scoped.

Completeness4/5

The data-research surface is unusually comprehensive: search, deep research, entity resolution/profiling, comparison, claim validation, alerts/subscriptions, and Toronto open-data querying are all covered. The main gaps are Toronto-side metadata details like resource schemas/columns and a way to browse the full dataset catalogue without a keyword.

Resources