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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, idempotentHint, and non-destructive behavior. The description adds behavioral details: it probes each entity with ai_visibility_check, ranks by score, and returns score, confidence, and signal density per entity. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph packed with information, including an example. While effective, it could be slightly restructured for scanning ease. No wasted words.

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 no output schema, the description explains the return format (ranked list with score, confidence, signal density). It covers purpose, usage, parameters, and behavior, making the tool self-contained and easy to invoke.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds meaning: entities first treated as subject, rest as competitors; models lists supported values; _apiKey clarifies conditionality; context explains disambiguation. This enhances understanding 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 clearly states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. This distinguishes it from sibling tools like ai_visibility_check (single probe) and compare_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 provides a clear use case (competitive AI-marketing audits) and an example question. It implicitly distinguishes from ai_visibility_check but does not explicitly state when not to use it or mention all 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

C2.8/5.0
Disambiguation2/5

Several tool families have blurry boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route into the same underlying catalog with only subtle differences, and the six polymarket_* tools overlap heavily (edge scanning vs. fill-risk vs. arbitrage vs. cross-venue spread). Compounding this, the server is named 'Giantbomb' but only 7 of 38 tools relate to that domain, so an agent cannot predict what this server does from its name.

Naming Consistency3/5

All tool names are snake_case, which helps, but the verb pattern is inconsistent: verb_noun (list_subscriptions, resolve_entity, validate_claim), bare verbs (remember, recall, forget), noun-prefix subjects (entity_profile, pipeworx_feedback, polymarket_edges, recent_changes), and even a versioned name (ask_pipeworx_beta) that breaks its own family's pattern. There is no predictable naming scheme an agent could use to guess a tool's name.

Tool Count2/5

38 tools is well past the 25+ 'too many' threshold, and the count is split across two unrelated concerns: ~7 Giant Bomb tools (several explicitly marked 'API offline as of 2026-05') and ~31 tools for a Pipeworx data-research and Polymarket-trading platform. The number is not merely heavy; it serves no single coherent purpose.

Completeness2/5

For the declared Giant Bomb domain, the surface is incomplete (missing videos, franchises, reviews, people, and other known resource types) and largely dead since the underlying API is offline. The Pipeworx half is comparatively complete with routing, grounded answers, research, memory, subscriptions, and feedback, but that coverage is attached to the wrong server identity and does not fill the gaps in the claimed purpose.