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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.

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds behavioral details: calls ai_visibility_check internally, ranks by score, surfaces most/least recognized, and returns score, confidence, signal density per entity. No contradictions 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?

Three sentences with clear structure: purpose, mechanics, use case. No redundant information. Well front-loaded with the core action.

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?

Without an output schema, the description thoroughly details return format (ranked list with score, confidence, signal density). It also covers parameter roles and typical use case, making it complete for a 4-param tool.

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?

Despite 100% schema coverage, the description adds significant value: explains entities ordering (first is 'subject'), default model, _apiKey optionality, and context purpose. This goes well 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's purpose: 'Compare AI visibility across multiple entities side-by-side.' It includes specific verbs like 'probes' and 'ranks', and distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (different 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 provides explicit context: 'Useful for competitive AI-marketing audits' and includes an example question. However, it does not explicitly state when not to use the tool or mention alternatives other than implying single-entity check via sibling name.

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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes, especially ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), ask_pipeworx_grounded, and deep_research. The Polymarket tools are more distinct, but the boundary between Maven search tools and broader discovery tools like search, search_by_coords, discover_tools, and suggest_questions is not always obvious.

Naming Consistency2/5

Naming is a mix of snake_case actions, branded prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*, plus inconsistent patterns like ai_visibility_check vs scan_competitor_ai_presence. Some clusters are internally consistent, but the overall set follows no predictable convention.

Tool Count2/5

35 tools is heavy for a server named 'Maven Central', and only a handful actually relate to Maven artifacts. The rest are Pipeworx research, prediction-market, memory, subscription, and utility tools, making the set feel sprawling rather than purpose-scoped.

Completeness3/5

For the Maven Central domain, search, coordinate lookup, version listing, and latest-version retrieval cover core read-only needs. However, there is no direct artifact metadata/POM/dependency inspection, and the unrelated Pipeworx and Polymarket tools dilute the surface without filling obvious gaps in the stated domain.

Resources