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Cataas

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral details: it probes each entity, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, signal density. This adds value beyond annotations without contradiction.

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, no redundant words. Key information is front-loaded (purpose and behavior), followed by use case and output format. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters and no output schema, the description adequately explains the process (probing, ranking, scoring) and output fields (score, confidence, signal density). It provides sufficient context for an agent to invoke 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% (all parameters described). The description adds meaning: 'first entry treated as subject for narrative' (for 'entities'), and clarifies usage of 'models' and 'context'. This provides extra context beyond the schema.

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 specific verbs ('Compare', 'Probes', 'ranks', 'surfaces') and clearly identifies the resource ('AI visibility across multiple entities'). It distinguishes itself from sibling tool 'ai_visibility_check' by focusing on side-by-side comparison of multiple entities.

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 states the use case ('competitive AI-marketing audits') and implies when to use: for comparing multiple entities. It doesn't explicitly say when not to use or name alternatives, but the context of probing with 'ai_visibility_check' suggests single-entity use is handled by that sibling.

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
Disambiguation2/5

Several tools intentionally overlap: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and ask_pipeworx/ask_pipeworx_grounded/deep_research plus discover_tools/suggest_questions sit close together. The long descriptions clarify differences, but an agent still has to choose between near-equivalent entry points.

Naming Consistency3/5

All names are readable snake_case, but there is no single consistent convention: verb-led names like list_tags and resolve_entity sit alongside noun-led names like random_cat, entity_profile, and polymarket_arbitrage. Domain prefixes like polymarket_ and pipeworx_ help, but the mixed grammar makes the surface less predictable.

Tool Count2/5

34 tools is far beyond what a cat-image server needs; only 3 tools relate to Cataas, while the rest form a sprawling Pipeworx data, prediction-market, memory, and subscription suite. The count is in the 'too many' range and most tools are outside the server's apparent stated domain.

Completeness3/5

The cat-image core covers random cats, tag-filtered cats, and tag listing, but omits other Cataas-style operations like fetching by cat ID or creating cat images with text/effects. The embedded Pipeworx side is broad, but it does not fill the gaps in the server's named cat API purpose.

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