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xkcd

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?

With strong annotations (readOnlyHint, idempotentHint), the description adds valuable behavioral details: it probes via ai_visibility_check, ranks by score, and returns a structured list. It does not mention potential rate limits or that multiple network calls may be slow, but the annotation coverage lowers the burden.

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, all substantive: function, mechanism, and output. Front-loaded with the main purpose, no redundant filler.

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?

Despite no output schema, the description specifies the return format ('ranked list with score, confidence, signal density'). Combined with comprehensive schema and annotations, the tool's behavior and constraints are fully documented for an agent to invoke it correctly.

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 covers 100% of parameters with detailed descriptions, including the 2-8 entity constraint and the first-entity-as-subject rule. The description does not add new parameter semantics beyond the schema, so the baseline score of 3 is appropriate.

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 single-entity tools like ai_visibility_check. It also states the ranking and output, making the tool's function 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 explicitly provides a use case ('competitive AI-marketing audits') and a concrete prompting example. It implies this tool is for multi-entity comparison rather than single checks, though it does not explicitly name alternatives or state when not to use it.

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

A3.6/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, and validate_claim all serve data-lookup purposes with unclear boundaries. The three xkcd comic tools are distinct but are buried under 31 unrelated tools, making selection confusing.

Naming Consistency2/5

Tool names follow no consistent pattern: some use verb_first (get_comic, list_subscriptions), some are nouns (entity_profile, deep_research), some have prefixes (ask_pipeworx_*, polymarket_*), and others are vague (scan_dependency, generate_llms_txt). The mixing of styles across the set makes it hard to predict naming.

Tool Count2/5

With 34 tools and a server name of 'xkcd', the count is wildly disproportionate; only 3 tools relate to comics. Even as a general data-access server, 34 tools is heavy and many are meta-tools (discover_tools, suggest_questions) that add bulk.

Completeness3/5

For the actual Pipeworx data domain, the surface is fairly comprehensive: querying, grounded answers, research, entity profiles, comparisons, validation, subscriptions, and prediction-market analysis are covered. However, there are notable gaps like no fetch-by-URI tool and no xkcd search/list capability, making the set incomplete for its name and slightly incomplete for its inferred domain.

Resources