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Glama

Zendesk

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 mark as read-only and idempotent. Description adds key behavioral details: probes each entity with ai_visibility_check, ranks by score, returns 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?

Two concise sentences plus a parenthetical example. Front-loaded with main purpose, no redundant fillers. Every sentence contributes.

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, description adequately explains return format (ranked list with score, confidence, signal density). Also specifies input constraints (2-8 entities). Sufficient for complex multi-probe tool.

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 (100% coverage). Description adds semantic value: explains 'entities' array (first entry as subject, rest as competitors) and purpose of 'context' parameter for disambiguation.

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' with specific verb (compare), resource (AI visibility), and scope (multiple entities). Distinguishes from sibling 'ai_visibility_check' by mentioning probing with that tool and ranking multiple 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?

Provides clear usage context: 'useful for competitive AI-marketing audits' with an example question. Implies single-entity checks should use the sibling tool, but does not explicitly state exclusions or 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

B3.3/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) which all provide factual answers, making it unclear which to use. The set also mixes Zendesk tools with a large number of prediction market and data tools, creating confusion.

Naming Consistency2/5

Naming conventions are inconsistent: Zendesk tools use 'zd_' prefix, Pipeworx tools use various prefixes like 'ask_', 'bet_', 'compare_', etc., and some are named with full words. There is no uniform pattern across the set.

Tool Count2/5

35 tools is high for a server named 'Zendesk', especially since only 5 are Zendesk-specific. The majority are unrelated to Zendesk, indicating the tool count is inappropriate for the implied purpose.

Completeness1/5

For a Zendesk server, the tool set is severely incomplete, offering only basic CRUD operations (get, list, search for tickets and users). Missing essential Zendesk features like ticket creation, update, delete, or macros, while containing many irrelevant tools from other domains.

Resources