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Always Seven

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly and idempotent behavior. The description adds context: it probes each entity with 'ai_visibility_check', ranks results, and returns scores, confidence, and signal density. This exceeds annotation coverage.

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 three sentences, no redundancy, front-loaded with the main purpose, then how it works, then an example use case. Every sentence earns its place.

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 no output schema, the description explains the return format (ranked list with score, confidence, signal density). It covers the main use case, the process, and parameter roles. Minor missing: no mention of rate limits or error handling, but not critical.

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 has 100% description coverage. The description adds meaning by explaining that the first entity is treated as the subject, that context is shared across probes, and that the tool ranks by score. This enriches understanding 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, using 'ai_visibility_check' internally, and ranks them. It distinguishes itself from the sibling 'ai_visibility_check' by focusing on side-by-side 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?

The description explicitly states it's useful for competitive AI-marketing audits and implies when to use (comparing multiple entities). It references the sibling tool indirectly by mentioning the internal use of 'ai_visibility_check', but lacks an explicit 'when not to use' statement.

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 overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying 5,708-tool catalog, and ask_pipeworx_beta is explicitly identical today. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also has fuzzy boundaries — both bet_research and polymarket_edges claim 'should I bet on X', and discover_tools versus suggest_questions both serve discovery. Despite very detailed descriptions, an agent would frequently struggle to pick the correct tool.

Naming Consistency3/5

The naming is mostly snake_case and readable, with coherent micro-families (polymarket_*, pipeworx_*, ask_pipeworx variants, remember/recall/forget). However, patterns are mixed: verb_noun (compare_entities, resolve_entity, generate_llms_txt) sits alongside noun-led names (entity_profile, recent_alerts, pipeworx_trending), and entity-related tools use three different conventions (compare_entities, resolve_entity, entity_profile). It's consistent within families but not across the full set.

Tool Count2/5

32 tools is well past the 25-tool threshold for a coherent set, and the scope is a scattered grab bag: a joke RNG, an AI-visibility probe, a 5,708-tool data router, prediction-market arb analytics, key-value memory, subscriptions, npm dependency scanning, llms.txt generation, and a feedback channel. Some of these are arguably platform additions rather than core tools, but as presented the count feels bloated and unfocused.

Completeness3/5

The major workflow areas are well covered — data lookup has routing, grounded mode, deep research, entity resolution, comparisons, profiles, and change feeds; memory and subscriptions each have full lifecycle coverage. However, the server repeatedly references pipeworx:// citation URIs as fetchable yet provides no record-fetching tool, and the diffuse purpose makes it hard to assess what 'complete' even means. Notable gaps exist around citation resolution and execution of the arbitrage signals the Polymarket tools generate.

Resources