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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

A4.2/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description discloses that probing Anthropic requires a BYO API key with direct user payment, default model is free, and return format includes per-model and combined views. 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 a single, well-structured paragraph of three sentences. It front-loads the primary action, then covers details and use cases, with no redundancy or filler.

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?

For a tool with no output schema, the description adequately explains the return format (per-model score, confidence, signals, raw_response + combined view) and use cases. It lacks detail on 'signals' and the combined view structure, but annotations provide safety context.

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 description coverage is 100% and adequately describes each parameter. The description adds minor value (e.g., default model name, _apiKey passthrough) but does not significantly extend schema info.

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/product/topic and returns a visibility score. It distinguishes itself from sibling tools like scan_competitor_ai_presence and ask_pipeworx by focusing on AI visibility metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists use cases (AI-marketing audits, pre-launch checks) but does not explicitly guide when to use this tool vs alternatives or when not to use it. No exclusion criteria or alternative tool references.

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
Disambiguation3/5

Tool purposes are largely distinct across domains (Jira, data queries, prediction markets), but there is overlap within domains, e.g., ask_pipeworx vs ask_pipeworx_grounded and multiple Polymarket edge analysis tools.

Naming Consistency2/5

Naming mixes multiple conventions: jira_ prefix, pipeworx_ prefix, polymarket_ prefix, and plain verbs (remember, forget, recall). No consistent pattern across the entire set.

Tool Count2/5

34 tools is excessive for a server named 'Jira' which only has 4 Jira-specific tools. The majority are unrelated Pipeworx and Polymarket tools, making the scope bloated.

Completeness2/5

Jira-specific tools are incomplete (missing create, update, delete). While Pipeworx and Polymarket tools are comprehensive, they are out of place for a Jira server, leaving gaps in its stated purpose.

Resources