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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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral details beyond annotations: it explains that the tool probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. No contradiction with 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?

The description is two sentences long, front-loaded with purpose, and includes an illustrative example and output details. Every sentence earns its place with no filler.

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?

With 4 parameters fully described in schema, no output schema, the description explains the return format (ranked list with score, confidence, signal density per entity) and constraints (2-8 entities). It is complete for an agent to use correctly.

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 description coverage is 100%, but the description adds key context not in schema: 'First entry treated as the "subject" for narrative; rest are competitors.' It also clarifies default models and when _apiKey is needed. This adds significant meaning 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 begins with a clear verb+resource statement: 'Compare AI visibility across multiple entities side-by-side.' It specifies the action (compare/scan), the resource (AI presence), and the scope (multiple entities). This distinguishes it from the sibling tool ai_visibility_check, which presumably checks a single entity.

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 explicitly states when to use: 'Useful for competitive AI-marketing audits' and gives an example question. It implies that for a single entity, one would use ai_visibility_check, but does not explicitly state when not to use this tool or mention alternatives beyond sibling context. The sibling list includes ai_visibility_check, which aids differentiation.

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

A3.9/5.0
Disambiguation3/5

Several tools cluster around the same core purpose: the three ask_pipeworx variants, the three census reverse-geocoders, and the six Polymarket analysis tools. Descriptions are detailed enough to disambiguate most choices, but ask_pipeworx_beta is currently identical to ask_pipeworx, creating genuine ambiguity. An agent could easily select the wrong tool in these overlapping families.

Naming Consistency3/5

Names mix verb-initial actions (ask_pipeworx, compare_entities, resolve_entity) with noun-initial compound names (census_block, entity_profile, polymarket_edges). The polymarket_* family is internally consistent, but the set as a whole lacks a uniform verb_noun convention. Single-word verbs like remember, recall, and forget further break the pattern.

Tool Count2/5

At 34 tools, this exceeds the 'too many' threshold of 25 and includes clear redundancy: ask_pipeworx_beta duplicates ask_pipeworx, county_for_point is a thin wrapper over the same service as census_area/census_block, and scan_competitor_ai_presence just loops ai_visibility_check. The broad scope does not justify this many tools, and the set would be better split into focused servers.

Completeness4/5

Each sub-domain has solid lifecycle coverage: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and company research has resolve_entity/entity_profile/compare_entities/recent_changes. Minor gaps exist (e.g., no direct pipeworx:// citation-fetching tool), but no critical dead ends that would cause agent failures.

Resources