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Glama

Tarot Draw

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.

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, destructiveHint=false, and openWorldHint. Description adds context: probes each entity with ai_visibility_check, ranks by score, and returns ranked list. No contradictions; description adds behavioral details beyond annotations.

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?

Two well-structured sentences. First sentence explains core function and behavior; second gives use case and output. No wasted words, efficient yet informative.

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?

Despite lacking output schema, description specifies return fields (score, confidence, signal density per entity). Combined with rich annotations and schema, it provides all necessary information for correct invocation.

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 covers all 4 parameters with descriptions (100% coverage). Description reinforces meaning by noting entities array size constraint and first entry as 'subject', and provides example usage for context and models. Adds value 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?

Description clearly states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks scores, and identifies most/least recognized. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities by focusing on competitive AI marketing audits.

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 states usefulness for competitive AI-marketing audits with an example question. Implies when to use (multiple entities) vs when not (single entity, use ai_visibility_check). Does not explicitly list exclusions or alternatives but provides strong contextual guidance.

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

A3.7/5.0
Disambiguation2/5

Multiple tool families have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, suggest_questions and discover_tools are near-duplicates, and ai_visibility_check is a subset of scan_competitor_ai_presence. The five polymarket_* tools are heavily overlapping in purpose and rely on long descriptions to distinguish them, which an agent must read carefully to avoid misselection.

Naming Consistency3/5

All names are lowercase snake_case and there are helpful prefixes (polymarket_*, ask_pipeworx_*, pipeworx_*), but the verb/noun ordering is inconsistent: verb-first names (generate_llms_txt, resolve_entity, scan_dependency) sit alongside noun-first names (bet_research, entity_profile, recent_changes) and bare verbs (forget, recall, remember). Sub-families are internally consistent, but the set as a whole follows no single convention.

Tool Count2/5

32 tools is over the 'too many' threshold, and the scope is a grab-bag rather than a focused server: data research, prediction-market analysis, npm dependency checks, llms.txt generation, memory utilities, subscriptions, and exactly one tarot tool. The server is named 'Tarot Draw' yet 31 of 32 tools serve a completely different purpose, making the count wildly mismatched to the apparent identity.

Completeness2/5

For the inferred Pipeworx data/prediction-market domain the coverage is genuinely deep — ask/grounded/deep research, entity resolution, profiles, comparisons, validation, subscriptions, alerts, edge tracking, and arbitrage all exist. But for the stated purpose ('Tarot Draw'), the surface is one draw tool with no deck details, spreads, reading history, or reversal support, and the data tools' domain is so diffuse that an agent cannot rely on the set forming a coherent workflow.

Resources