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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 declare readOnly, openWorld, and idempotent, so the description adds valuable non-redundant context: default free model, BYO key cost implications ('you pay Anthropic directly'), and the exact return structure per model. This goes beyond the safety profile captured 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?

Four dense sentences with front-loaded action. Every sentence earns its place: purpose, default behavior, return format, and use cases. No fluff or repetition of schema details.

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?

Given the tool has no output schema, the description compensates by specifying per-model return fields ({score, confidence, signals, raw_response}) and a combined view. It also covers cost behavior and default model, which is essential for an agent deciding whether to use it. This is complete for the tool's complexity.

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 the baseline is 3. The description adds confirmation of the default model and that _apiKey enables Anthropic, but it doesn't meaningfully enhance parameter understanding beyond the schema. The return-format info is output semantics, not parameter semantics.

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 a clear resource ('one or more LLMs'), then states the scoring output (0-100) and use cases. It clearly distinguishes itself from siblings like scan_competitor_ai_presence by focusing on generic visibility scoring across models rather than competitive scanning.

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 context: default model, when to pass _apiKey, and use-case categories (AI-marketing audits, pre-launch brand checks, competitive monitoring). It doesn't name specific sibling tools as alternatives, but the guidance is sufficient for an agent to decide when to invoke 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

Several tools overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as query routers, with the beta variant currently identical to the stable one. However, most other tools have clearly distinct purposes (memory, subscriptions, prediction market analytics), and the detailed descriptions help differentiate them.

Naming Consistency4/5

All tool names use snake_case and are descriptive, with consistent domain prefixes like pipeworx_ for meta tools and polymarket_ for prediction markets. Some names mix noun-phrase and verb-noun patterns (e.g., ai_visibility_check vs. resolve_entity), but the overall style is predictable and readable.

Tool Count2/5

With 31 tools, the server exceeds the 25-tool threshold for a coherent set. While the broad scope (data querying, prediction markets, memory, subscriptions, AI visibility) justifies many tools, the sheer number creates cognitive load and makes selection harder for agents.

Completeness4/5

The tool surface is quite comprehensive for its domains: querying has ask_pipeworx, grounded answer, deep research, entity profiles, comparisons, and claim validation; prediction markets have research, arbitrage, edge tracking, and fill risk; memory and subscription lifecycles are covered. Minor gaps exist (e.g., no subscription update) but are workable.

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