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nouel

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive. The description adds meaningful behavioral context beyond that: default free model, BYO key for Anthropic (with direct cost implication), and the exact return shape per model. This is rich, non-redundant disclosure.

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, zero filler. The purpose is front-loaded, followed by default behavior, return format, and use cases. Every sentence carries information.

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 having no output schema, the description compensates by specifying per-model {score, confidence, signals, raw_response} plus a combined view. It also covers model selection, cost, and practical use cases, making the tool's behavior fully comprehensible for selection and invocation.

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 coverage is 100%, so baseline is 3. The description reinforces that `_apiKey` enables Anthropic probing and that the default model is Workers AI, but it does not add meaning beyond the schema's own parameter descriptions. No deduction or bonus.

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 states a specific action ('Probe one or more LLMs') with a clear resource and measurable output ('score visibility (0-100) per model'). It also names concrete use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) that differentiate it from sibling tools like ask_pipeworx 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 gives clear when-to-use guidance via use cases, but does not explicitly discuss when not to use the tool or name alternative tools. This is 'clear context, no exclusions,' so a 4 fits.

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

A3.8/5.0
Disambiguation3/5

Several tool groups have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research all query Pipeworx data; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker all analyze prediction markets). Descriptions help distinguish them, but the boundaries are not always clear.

Naming Consistency3/5

Tool names are mostly descriptive but mix conventions: some are verb_noun (list_subscriptions, get_prizes_by_year), others are noun_verb (pipeworx_feedback, polymarket_edges), and a few are single verbs (remember, recall). No strong pattern, but still readable.

Tool Count4/5

With 32 tools, the server covers a wide range of domains (data querying, prediction markets, company analysis, Nobel prizes, memory, subscriptions). The count is high but each tool serves a specific purpose, justifying the breadth.

Completeness4/5

The tool set provides comprehensive coverage for its stated domains: data retrieval, entity resolution, comparison, monitoring, and memory. Minor gaps exist (e.g., no direct bet placement on Polymarket), but core workflows are well-supported.

Resources