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Bioregistry

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?

The description explicitly states internal behavior: 'Probes each entity with ai_visibility_check, ranks by score, surfaces which is most/least recognized.' It also describes the output: 'Returns ranked list with score, confidence, signal density per entity.' This adds value beyond the annotations, which already indicate readOnly and idempotent behavior.

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: first covers purpose and process, second provides a use case and output details. It is front-loaded with the main action and avoids unnecessary words. Every sentence serves a purpose.

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 tool's complexity (4 parameters, no output schema, but comprehensive annotations), the description covers purpose, usage, behavioral details, parameter roles, and output format. It is fully complete for an agent to understand and invoke 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?

The description adds significant meaning beyond the schema: 'First entry treated as the "subject" for narrative; rest are competitors.' This clarifies the 'entities' parameter's semantics. The schema has 100% coverage, but the description enriches understanding. No parameter is left ambiguous.

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 specifies the verbs (compare, probes, ranks) and the resource (AI visibility). It distinguishes from sibling tools like ai_visibility_check by emphasizing multiple entities and competitive analysis.

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: 'Useful for competitive AI-marketing audits' with an example question. It implies when to use but does not explicitly list when not to use or compare with alternatives. However, the context is sufficient for an agent to decide.

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

Severe overlap exists between ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all of which route to the same 5,743-tool catalog with only subtle differences. The pair resolve (CURIE-to-URL) and resolve_entity (name-to-ID) share the same verb but mean completely different things in different domains, and the five polymarket tools have heavily overlapping purposes.

Naming Consistency2/5

The set mixes multiple conventions: noun-only names (prefix, prefixes, search, resolve), verb_noun names (generate_llms_txt, scan_dependency, validate_claim), adjective_noun names (recent_alerts, recent_changes), and vendor-prefixed names (ask_pipeworx*, pipeworx_*, polymarket_*). Some names break the pattern entirely, like forget and remember, and the plural prefix/prefixes pair is inconsistent.

Tool Count2/5

35 tools is heavy for any single server, but the bigger problem is that roughly 31 tools are Pipeworx platform utilities (asking, memory, subscriptions, feedback) while only 4 serve the stated 'Bioregistry' purpose. A server named Bioregistry carrying prediction-market arbitrage and AI-visibility tools is poorly scoped regardless of the absolute count.

Completeness2/5

The Bioregistry surface is thin: search, prefix, prefixes, and resolve cover lookup/pagination but no registry management, and the remaining tools belong to an entirely different, unrelated domain. The server's apparent purpose ('Bioregistry') is barely served, while the Pipeworx functionality, though broad, is buried under an incongruent server name, making the overall surface incomplete and incoherent.

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