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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

A4.8/5.0
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive; description adds detail on default model, API key usage, and per-model return structure, enriching the behavioral model.

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 concise sentences cover purpose, model details, output format, and use cases, front-loaded with no fluff.

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, description clearly describes return structure (per-model score, confidence, raw response); all necessary information for usage is present.

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

Parameters5/5

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

Schema descriptions cover all 4 parameters (100% coverage); description adds context like default model and API key purpose, and explains the context parameter's role in disambiguation.

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?

Description clearly states it probes LLMs for brand visibility and returns per-model scores, distinguishing it from sibling tools like deep_research or scan_competitor_ai_presence through specific output details.

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?

Explicitly lists use cases (AI-marketing audits, pre-launch checks, competitive monitoring) but does not contrast with similar sibling tools 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

A4/5.0
Disambiguation3/5

Many tools serve similar querying purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) which could confuse an agent. However, detailed descriptions clarify differences, and some tools are very distinct (e.g., CIDR parsing, entity profile). Overlap is moderate but not severe.

Naming Consistency5/5

Tool names follow a consistent verb_noun pattern (e.g., resolve_entity, validate_claim, list_subscriptions) with underscores separating words. No mixing of styles like camelCase or abbreviations. Naming is clear and predictable.

Tool Count2/5

33 tools is excessive for a single server, covering areas as diverse as IP parsing, AI visibility, Polymarket betting, and SEC filings. This broad scope suggests the server tries to do too much, leading to a heavy and potentially unwieldy tool set.

Completeness3/5

The tool surface covers many domains (financials, prediction markets, IP tools, memory, subscriptions) but has notable gaps: no update for stored memories, limited subscription management (no modification), and some tools are marked as beta or deprecated. The set feels broad but not deep.

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