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Biostudies

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

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds that it 'probes each entity with ai_visibility_check' and returns a ranked list. However, it does not disclose the multiple API calls, potential latency, or cost implications. Adequate but not rich.

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?

Two sentences: first states the core action and output, second gives a concrete use case and lists returned fields. Every word earns its place; no fluff. Front-loaded with the main purpose.

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?

No output schema exists, so the description covers the return format (ranked list with score, confidence, signal density). It explains the internal mechanism briefly (probes with ai_visibility_check) and the first-entity treatment. It does not specify error handling or edge cases, but for most agents it's sufficient.

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%, so baseline is 3. Description adds value by explaining that the first entity is the 'subject' for narrative and that 'workers-ai' is free default while 'anthropic' requires an API key. This enriches the user's 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?

Description uses specific verb+resource: 'Compare AI visibility across multiple entities side-by-side'. It clearly distinguishes from its likely sibling `ai_visibility_check` (single entity) by focusing on comparison and ranking. The phrase 'most/least recognized' adds clarity.

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?

Explicitly states use case: 'competitive AI-marketing audits' with an illustrative question. Implies when to use (comparison, not single check) but does not explicitly exclude alternatives or mention when not to use. Clear enough for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Biostudies toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools occupy nearly identical roles: ask_pipeworx and ask_pipeworx_beta are described as functionally identical right now, ask_pipeworx_grounded and deep_research are overlapping query modes, and ai_visibility_check / scan_competitor_ai_presence / discover_tools / suggest_questions all blur into discovery or visibility tasks. The two actual BioStudies tools are clear, but they are buried in a server dominated by Pipeworx meta-tools.

Naming Consistency3/5

All tool names use snake_case, and many follow a verb_noun shape such as search_studies, get_study, and discover_tools. However, the convention is inconsistent across the set: noun-first names like entity_profile and polymarket_edges, brand-prefixed names like pipeworx_feedback, and verb-first product names like ask_pipeworx all coexist, making the pattern harder to predict.

Tool Count2/5

33 tools is well into the too-many range, and the count is especially inappropriate for a server named Biostudies since only search_studies and get_study actually belong to that domain. The rest form a sprawling general-purpose data-research platform that appears to have been merged into one server without a clear scope.

Completeness3/5

For the BioStudies-specific surface, search_studies and get_study provide reasonable read-only coverage for the EBI archive. But as the broader research platform the other 31 tools imply, the set is hard to evaluate for completeness because most actual data access is delegated to Pipeworx's hidden 5,718 tools rather than exposed directly, leaving notable gaps in transparency and direct source-level control.

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