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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral detail: it probes each entity with ai_visibility_check and returns ranked list with score, confidence, signal density. No contradictions.

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 core purpose, no fluff. Every sentence adds value.

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 params (1 required), no output schema, the description sufficiently explains what the tool does, how it works internally, and what the return structure includes.

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% with descriptions. The description adds extra meaning: first entity treated as 'subject' for narrative, models defaults to just workers-ai. This goes beyond 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 clearly states the tool compares AI visibility across multiple entities, using ai_visibility_check internally, and ranks them. It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (general 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?

The description explicitly says 'useful for competitive AI-marketing audits' and gives a concrete example question. It implies when to use but does not state when not to use or list 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

A3.6/5.0
Disambiguation2/5

The five yt_* tools are distinct, but the majority of the set is dominated by overlapping Pipeworx tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points, and the multiple polymarket_* tools cover closely related edge/arbitrage/fill-risk territory. An agent could easily select the wrong one.

Naming Consistency3/5

Most names use lowercase snake_case, but conventions are mixed: yt_* and polymarket_* are prefix-scoped, pipeworx_* mixes verb-first and noun-first names, and there are standalone verbs like remember, recall, and forget. The server is named Youtube, yet the bulk of tools follow unrelated Pipeworx/Polymarket naming.

Tool Count2/5

36 tools is heavy for a server named Youtube, and only 5 actually address YouTube functionality. The remaining 31 tools cover unrelated data-research and prediction-market features, making the surface bloated and off-purpose.

Completeness3/5

The YouTube subset covers search, channel info, channel videos, video details, and comments, which handles basic read-only queries. Missing playlists, captions/transcripts, and subscription/upload actions leave notable gaps for a YouTube-focused server.