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Glama

Coingecko

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. Description adds behavioral context: it probes with ai_visibility_check, returns ranked list with score/confidence/signal density, and explains model options (workers-ai default, anthropic requires _apiKey). 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.

Conciseness4/5

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

Two sentences, front-loaded with main action. Information-dense but not overly concise. Could tighten slightly (e.g., reduce example use case) but overall no wasted words.

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?

Given 4 parameters and no output schema, description covers inputs, process, output format, and use case. Missing error handling or limits (max 8 entities from schema), but sufficient for typical usage.

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 description adds meaning: 'first entry treated as subject', 'shared context', and specific model support details (free default vs anthropic with key). These clarify parameter usage beyond schema descriptions.

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') and clearly states the resource ('AI visibility across multiple entities'). It distinguishes itself from siblings like 'ai_visibility_check' (single entity) by emphasizing side-by-side comparison, and from 'compare_entities' by specifying the AI visibility context.

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?

Description explicitly states when to use ('competitive AI-marketing audits') and what it does (probes each entity with ai_visibility_check, ranks). It implies when not to use (for single entity check, use sibling), but lacks explicit exclusions or alternatives. Still clear and helpful.

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

Several tools are near-duplicates or have heavily overlapping responsibilities (ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, polymarket_arbitrage / polymarket_edges / polymarket_fill_risk, and ai_visibility_check / scan_competitor_ai_presence). The many meta/entry-point tools (discover_tools, suggest_questions, pipeworx_trending) also blur the boundary between discovery and execution.

Naming Consistency2/5

Tool names mix imperative verb-first patterns (get_coin, search_coins, validate_claim) with noun-phrase labels (bet_research, entity_profile, pipeworx_trending, polymarket_edge_tracker) and inconsistent prefixes. Snake_case is consistent, but the naming grammar and verb styles are not.

Tool Count2/5

35 tools is well past the comfortable range, and the count feels inflated by duplicate routing modes, overlapping polymarket scanners, and generic memory/meta utilities. A server nominally named Coingecko carries only 4 crypto tools while the overwhelming majority belong to unrelated domains.

Completeness2/5

As a CoinGecko server, the surface is severely incomplete: search/get/market/trending exist but historical prices, OHLC, exchanges, categories, and coin details are missing. As a broader data/prediction-market utility it is more expansive, but the lack of a coherent domain makes coverage impossible to assess as a single product.

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