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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

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: the default free model, the BYO key for Anthropic, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). This fully complements the readOnlyHint, idempotentHint, and openWorldHint 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 concise (two sentences), front-loaded with the core purpose, and every sentence adds essential information. No unnecessary words.

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 4 parameters, no output schema, and no nested objects, the description provides a complete picture: it explains what the tool does, how parameters affect behavior, and what the return format includes (per-model fields + combined view). The use cases are also stated.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining the default model for the 'models' parameter, the purpose of '_apiKey' (BYO key for Anthropic), and the disambiguation role of 'context'. This goes beyond the schema's descriptions.

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's purpose: probing LLMs to score visibility for a given entity. It uses specific verbs (probe, score) and resource (LLMs, visibility), and distinguishes from sibling tools like 'ask_pipeworx' and 'deep_research' by focusing on AI visibility audits.

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 explicitly mentions use cases like 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It provides context for when to use optional parameters (e.g., anthropic requires _apiKey). However, it does not explicitly state when not to use or contrast with specific alternatives.

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

A4.1/5.0
Disambiguation3/5

Several tool pairs have blurred boundaries: 'ask_pipeworx', 'ask_pipeworx_beta', and 'ask_pipeworx_grounded' serve overlapping routing purposes with minor differences in grounding or versioning, which can confuse an agent. Similarly, 'pipeworx_feedback' and the user feedback mechanism inside other tools lack clear tool-level distinction. Most other tools are distinct but the cluster of ask_pipeworx variants lowers overall clarity.

Naming Consistency4/5

Tool names largely follow a consistent verb_noun or prefix_noun pattern (e.g., ask_pipeworx, resolve_entity, scan_dependency). Some names like 'bet_research' and 'datasets' deviate from this pattern but remain readable. No chaotic mixing of conventions like camelCase and snake_case is present, so consistency is high overall.

Tool Count3/5

With 34 tools, the count is on the higher side for a single server, yet the tool set covers a broad domain of data access, analysis, and monitoring (data pipelines, prediction markets, compliance scans). Given the variety of distinct capabilities offered, 34 is borderline but not excessive enough to drop to a 2, as each tool addresses a concrete need.

Completeness5/5

The tool set offers a remarkably complete lifecycle for data operations: discovery (suggest_questions, discover_tools, datasets), entity resolution (resolve_entity, metadata), querying and retrieval (ask_pipeworx, deep_research, query), analysis and comparison (compare_entities, entity_profile, validate_claim), memory (remember, recall, forget), monitoring (subscribe, recent_alerts, polymarket_edge_tracker), and feedback (pipeworx_feedback). Niche tools like bet_research, scan_dependency, and generate_llms_txt further fill domain-specific gaps. No obvious missing operations for the stated purpose.

Resources