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Caniuse

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context: default model is free Workers AI Llama-3.3-70b, passing _apiKey probes Anthropic and incurs direct costs, and return format is specified. No contradiction 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?

Four sentences, each earning its place: core function, default/config option, return format, and use cases. Front-loaded with the primary purpose, no redundancy or fluff.

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 no output schema, the description specifies return fields ({score, confidence, signals, raw_response} + combined view), explains the default model and optional API key, and gives use cases. This is sufficient for an agent to select and invoke the tool correctly.

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 coverage is 100%, but the description enriches meaning for key parameters: 'models' defaults to Workers AI unless _apiKey is provided, and _apiKey carries cost implications for Anthropic calls. This goes beyond the schema's basic parameter 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 uses a specific verb ('Probe') and resource ('LLMs') and clearly defines the output ('score visibility (0-100) per model'). This distinguishes it from sibling tools like ask_pipeworx or deep_research, which serve different purposes.

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 lists explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly state when not to use the tool or name alternatives, but the context is clear.

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
Disambiguation2/5

The tool set has significant overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and deep_research, entity_profile, compare_entities, recent_changes, and validate_claim all retrieve structured data with overlapping capabilities. The five Polymarket-oriented tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) further blur boundaries. Agents will struggle to select the right tool without reading very long descriptions.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (ask_pipeworx, compare_entities, validate_claim), and the polymarket_* cluster is consistently prefixed. However, a few tools are bare nouns (feature, support, search) and the remember/forget/recall trio deviates from the dominant pattern, creating minor inconsistency.

Tool Count2/5

35 tools is excessive for a server named 'Caniuse' — only 4 tools actually pertain to browser compatibility (feature, support, search, list_browsers), while 31 are Pipeworx data tools. The server name misrepresents the content, and the sheer number overwhelms rather than scopes the surface.

Completeness3/5

For the caniuse domain, coverage is complete (search, feature, support, list_browsers). The Pipeworx side includes meta-tools (discover_tools, suggest_questions), retrieval, memory, subscriptions, and feedback, but some tools require accounts and there are gaps like no direct way to list all data sources without discover_tools. The overall surface is broad but lacks obvious missing operations for any single coherent domain.

Resources