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Ror

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

A4.5/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, open-world, and non-destructive behaviors. The description adds substantial context beyond those flags: it discloses orchestration of ai_visibility_check, describes ranking logic, and specifies the exact output content (score, confidence, signal density). This is genuinely useful behavioral detail.

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?

Four sentences, each earning its place: purpose, mechanism, use case, and output. The information is front-loaded and free of filler. The example quote is illustrative, not fluff.

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?

Given the lack of an output schema, the description compensates by explicitly naming return fields (ranked list, score, confidence, signal density). It also covers the tool's internal workflow and the competitive audit context, leaving no critical gap for deciding when and how to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides per-parameter descriptions for all four parameters (100% coverage). The description doesn't add syntax, constraints, or parameter-specific meaning beyond what the schema already offers. It reaches the baseline of 3, as the schema carries the semantic burden.

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 opens with a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It names the underlying tool (ai_visibility_check), indicates ranking/surfacing behavior, and clearly distinguishes itself from siblings by focusing on multi-entity competitive comparison. The use-case quote reinforces the intended scenario.

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 positions the tool for 'competitive AI-marketing audits' and contrasts 'your brand + N competitors', giving clear contextual when-to-use guidance. However, it does not explicitly name alternative tools or state when not to use it, so a minor deduction for missing exclusions.

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

A3.5/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps with ask_pipeworx, and discover_tools/suggest_questions both serve a discovery role. The extremely detailed descriptions help, but the boundaries between meta-tools and research tools are genuinely confusing, especially with 34 tools in one namespace.

Naming Consistency2/5

Naming is a mix of bare verbs (get, search, recall), nouns (affiliation, entity_profile, recent_changes), and verb_noun phrases (resolve_entity, compare_entities, validate_claim). Some families are consistent (polymarket_*), but overall there is no uniform convention or prefix scheme, making the set feel arbitrary.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many.' While the broad data-platform scope explains some of the count, many tools are meta-utilities (feedback, trending, memory, subscription management) and there are near-duplicate variants (three ask_pipeworx forms, six Polymarket tools) that inflate the surface.

Completeness4/5

For a data-research platform the surface is impressively complete: lookup, grounded verification, entity profiles, comparisons, claim checking, subscription lifecycle, memory, and tool discovery are all covered. Minor gaps exist (e.g., no direct general-purpose web fetch, and ROR lacks create/update, which is acceptable for a curated registry), but agents should rarely hit dead ends.

Resources