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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

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds value by disclosing cost implications (free default vs. BYO key for Anthropic) and the return format per model. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured paragraph that front-loads the main action, default behavior, key condition (API key), return format, and use cases. Every sentence adds value with no redundancy.

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?

Given no output schema, the description explains the return structure (per-model fields + combined view). It covers parameter usage and main use cases. There is no need for pagination or error handling details for this probing tool.

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 description coverage is 100%, so baseline is 3. The description adds context beyond schema: default model, API key format, and the role of the 'context' parameter for disambiguation. This meaningful addition justifies a score of 4.

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 probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100). It uses specific verbs ('probe', 'score') and identifies the resource ('LLMs'), distinguishing it from sibling tools like 'scan_competitor_ai_presence' or 'deep_research'.

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 explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and states when to provide an API key. However, it does not explicitly contrast with sibling tools or specify when not to use this tool.

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
Disambiguation3/5

Several clusters overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, and ai_visibility_check duplicates scan_competitor_ai_presence at a smaller scale. The detailed descriptions do separate most of these by routing, mode, or output, but the currently identical beta router and the broad ask/research family create real ambiguity.

Naming Consistency3/5

Names are all lowercase snake_case and there are coherent prefixes like polymarket_ and pipeworx_, but the set mixes imperative verb_noun names (list_subscriptions, validate_claim) with descriptive noun phrases (macro_snapshot, entity_profile, polymarket_edge_tracker) and bare verbs. The inconsistency is readable but not a single predictable pattern.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent server, and the set spans many unrelated domains: data routing, prediction markets, memory, subscriptions, AI visibility, package scanning, and llms.txt generation. Even if each cluster has a purpose, the server is overloaded and several high-level wrappers could be consolidated.

Completeness4/5

The main clusters are well covered: memory has remember/recall/forget, subscriptions have subscribe/list/recent_alerts/unsubscribe, and company research has resolve_entity, entity_profile, recent_changes, and compare_entities. Minor gaps exist (no direct trade placement, no general web search, no account/profile management), but agents can complete most workflows without dead ends.

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