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Caniuse

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

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, idempotent, non-destructive. Description adds that it makes multiple probes, ranks results, and returns score/confidence/signal density. This provides behavioral detail beyond the annotation flags.

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, front-loaded with the core function, each sentence adds useful context (purpose, use case, output). No redundancy.

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 schema's detailed parameter descriptions and annotations, the description covers output format and use case sufficiently. No output schema exists, but the description explicitly lists the return fields (score, confidence, signal density), making the tool's behavior predictable.

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?

Input schema covers 100% of parameters with descriptions. The description adds the 'your brand + N competitors' interpretation and mentions the probing mechanism, but doesn't substantially extend parameter meaning beyond what's in schema. Baseline 3 applies.

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?

Description states verb 'Compare' and resource 'AI visibility across multiple entities', specifies it probes with ai_visibility_check, ranks by score, and identifies most/least recognized. This clearly distinguishes it from sibling ai_visibility_check (single-entity) and compare_entities (generic).

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?

Description states usefulness for 'competitive AI-marketing audits' with an illustrative question. Implicitly contrasts with single-entity ai_visibility_check by saying 'across multiple entities side-by-side' and 'probes each entity'. Lacks explicit when-not-to-use or named alternatives, but 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