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Apology

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

A4.6/5.0
Behavior5/5

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

The description adds substantial behavior beyond annotations: it explains that each entity is probed with ai_visibility_check, results are ranked by score, and the first entity is treated as the subject. Annotations confirm read-only, idempotent, non-destructive behavior, and the description aligns with these, providing clear operational details.

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 with front-loaded main action, clear explanation of internal process, and a concrete use case. Every sentence contributes meaning, no filler or repetition.

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 (4 params, no output schema), the description adequately describes the return format (ranked list with score, confidence, signal density) and the internal mechanism. It does not mention potential performance implications of calling multiple probes, but the agent can infer this. Overall complete enough for correct selection and invocation.

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% with good descriptions. The description adds value by explaining that the first entity in 'entities' is treated as the subject for narrative, and provides an example for 'context' (e.g., 'B2B SaaS'). This enriches the schema without redundancy.

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 side-by-side using ai_visibility_check, ranks them, and identifies most/least recognized. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities by specifying the internal mechanism and competitive audit use case.

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 provides a clear use case ('competitive AI-marketing audits') and an example question, implicitly guiding when to use. However, it does not explicitly mention when not to use or list alternative tools like running ai_visibility_check separately. The context is adequate but not exhaustive.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates with beta explicitly identical to the stable version, causing potential misselection. ai_visibility_check and scan_competitor_ai_presence overlap heavily, and resolve_entity/discover_tools/ask_pipeworx all serve lookup purposes. Many tools are distinct, but the boundaries around the core query tools are blurry.

Naming Consistency2/5

Naming is inconsistent: mostly snake_case but mixed verb styles (ask_pipeworx vs pipeworx_feedback vs resolve_entity), brand prefixes applied irregularly, and no uniform convention (e.g., subscribe/unsubscribe/list_subscriptions vs forget/remember/recall vs polymarket_arbitrage/edges/edge_tracker). Some names are descriptive, but the set lacks a predictable pattern.

Tool Count2/5

32 tools is above the 25 threshold for a heavy surface, and the server mixes unrelated domains (data lookup, prediction markets, memory, subscriptions, AI visibility, apology generation). While a large data platform could justify many tools, the random inclusions (apology_generate, generate_llms_txt, scan_dependency) suggest a lack of scoping. Several tools could be consolidated without loss.

Completeness4/5

For the dominant data-research/prediction-market domain, coverage is strong: lookup, grounded verification, research, entity resolution, comparison, arbitrage scanning, fill risk, subscriptions, memory, and discovery are all present. Minor gaps exist (no direct account management beyond subscriptions, no tool to modify stored memories), but agents can mostly achieve their goals. The stray non-domain tools do not hurt completeness of the core platform.

Resources