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Predictit

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds specific behavioral details: probes each entity with ai_visibility_check, ranks, returns score/confidence/signal density.

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, front-loaded with action, no redundant information. Efficient and clear.

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?

Covers purpose, usage, return type (ranked list with metrics), and underlying tool call. Lacks error handling or limitations, but sufficient given annotations and schema richness.

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 baseline is 3. Description adds minimal extra beyond schema; mentions 'context' for disambiguation but doesn't elaborate on parameter specifics or examples.

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 clearly states 'Compare AI visibility across multiple entities side-by-side' and distinguishes from sibling 'ai_visibility_check' (single entity) and 'compare_entities' (generic comparison).

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?

Explicitly says 'Useful for competitive AI-marketing audits' and implies when to use via the example question. Does not explicitly state when not to use, but context hints at single-entity 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
Disambiguation4/5

Most tools have distinct purposes, but pairs like ask_pipeworx/ask_pipeworx_grounded and bet_research/polymarket_edges could cause confusion without careful reading. Overall, descriptions are clear enough to differentiate.

Naming Consistency3/5

Names are snake_case and mostly follow verb_noun pattern, but several are noun_noun (entity_profile, pipeworx_feedback, polymarket_arbitrage) creating inconsistency. Still readable due to descriptive terms.

Tool Count4/5

33 tools is slightly high but justified given the broad scope (data retrieval, prediction markets, memory, subscriptions). Each tool serves a specific role, so the count feels appropriate for the platform's capabilities.

Completeness4/5

The tool set covers data retrieval, prediction market analysis, memory management, and subscriptions well. Minor gaps exist (e.g., no direct betting tool), but core workflows are supported comprehensively.

Resources