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xkcd

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.4/5.0
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 beyond this by disclosing cost implications (BYO Anthropic key, pay directly, free default) and the return shape (per-model score, confidence, signals, raw_response). This is valuable behavioral context not present in 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 three sentences that front-load the main action, then efficiently cover default, cost, output, and usage cases. No filler or repetition; each sentence adds unique value.

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?

With no output schema, the description compensates well by describing the per-model result structure and combined view. It also covers defaults, cost, and use cases. Minor gap: it does not explain what 'signals' contains or how the score is computed, but the overall tool behavior is sufficiently complete for invocation.

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 baseline is 3. The description adds meaningful param context by explaining the default model and the purpose of _apiKey (to also probe Anthropic with BYO key and direct payment), which goes slightly beyond the schema's simple parameter descriptions.

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 opens with a specific verb ('Probe') and clearly defines the resource (one or more LLMs) and the output (visibility score 0-100 per model). This distinguishes it from siblings like ask_pipeworx, which answer questions rather than score AI visibility.

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?

Explicit use cases are given ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), which helps an agent decide when to invoke it. It does not explicitly name alternative tools or negative usage conditions, 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

A3.6/5.0
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