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Glama

Macvendors

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context: it internally calls ai_visibility_check per entity and returns a 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?

Two sentences, front-loaded with the main action and output. No filler; every sentence adds value. Structure is ideal for quick comprehension.

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 the tool's complexity (multi-entity comparison, ranking, multiple models), the description covers key aspects: input, process, output, and use case. No output schema, but return structure is described. Minor gap: no mention of error handling or model selection details.

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%, so baseline is 3. The description adds semantics: 'First entry treated as the subject for narrative' and explains that 'context' is a shared context for disambiguation. This adds value 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. It distinguishes from sibling tools like ai_visibility_check (single) 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?

Explicitly indicates use for competitive AI-marketing audits with an example question. Provides context but doesn't explicitly state when not to use or mention alternatives, though sibling differentiation is implied.

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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta variant currently identical to stable. The five polymarket_* tools plus bet_research also blur boundaries, and discover_tools/suggest_questions both serve discovery. Detailed descriptions help, but an agent could easily select the wrong tool.

Naming Consistency3/5

All names use lower_snake_case and many follow a clear verb_noun pattern (resolve_entity, validate_claim, generate_llms_txt). However, there are notable deviations: entity_profile, pipeworx_feedback, polymarket_edges, recent_changes, and bet_research lead with nouns or adjectives. The style is readable and predictable in clusters, but not uniform.

Tool Count2/5

32 tools is well above the 25+ threshold for 'too many', and the server name Macvendors suggests a narrow MAC-lookup service, yet most tools belong to a much broader Pipeworx data/prediction-market platform. Several tools are near-duplicates or micro-variants (three ask_pipeworx versions, six polymarket tools). The set would be more appropriately split or heavily consolidated.

Completeness4/5

Within the actual broad research platform domain, the surface is fairly complete: discovery, routing, grounded answers, entity profiles, comparisons, recent changes, claim validation, semantic search, prediction-market analysis, subscriptions, memory, and feedback are all covered. Minor gaps exist, such as no subscription update tool and no batch MAC lookup, but agents can generally complete workflows without dead ends.

Resources