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Biosamples

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds cost implications (free default, BYO key for Anthropic) and return structure (per-model fields + combined view). No contradiction 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?

The description is concise (3 sentences) with front-loaded purpose. Every sentence provides essential information: what it does, default model, optional API key, return format, and use cases. No redundant or missing content.

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?

Despite no output schema, the description explains return values sufficiently (per-model {score, confidence, signals, raw_response} + combined view). It covers all aspects for a probing tool with 4 parameters and full schema descriptions.

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 parameters are documented. The description adds value by naming the default model ('Workers AI Llama-3.3-70b'), clarifying payment for Anthropic, and explaining the optional context parameter's purpose.

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: probing LLMs for brand visibility and scoring it 0-100. It uses specific verbs ('probe', 'score') and resource ('LLMs'). It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring across multiple models.

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 use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use the default model vs Anthropic with a BYO key. It lacks explicit when-not-to-use or alternatives, but the context is clear.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical to ask_pipeworx right now), and ask_pipeworx_grounded are three variants of the same router, while six polymarket_* tools plus bet_research all target prediction-market edge discovery. ai_visibility_check and scan_competitor_ai_presence further overlap. Only a minority of the 33 tools have clearly distinct purposes.

Naming Consistency3/5

snake_case is used throughout, and the polymarket_/pipeworx_ prefixes are internally consistent, but the naming convention mixes verb_noun (ask_pipeworx, search_samples, resolve_entity, validate_claim) with noun-first names (entity_profile, recent_changes, bet_research) and ad-hoc names like discover_tools or generate_llms_txt. Readable, but no single predictable pattern.

Tool Count1/5

33 tools for a server named 'Biosamples' is an extreme scope mismatch: only 2 of 33 tools (get_sample, search_samples) relate to biological samples, with the remaining 31 forming an unrelated kitchen sink of Pipeworx data routing, Polymarket betting, npm dependency checks, AI visibility audits, memory storage, and subscription management. The count is far beyond anything the stated purpose justifies.

Completeness2/5

For the actual Biosamples domain, search + get covers read-only access but no submission, annotation, or batch workflows, and the server's stated purpose is drowned out by unrelated domains that are only partially covered. The surface is simultaneously bloated with 31 irrelevant tools and thin on the one domain the server name promises, making coherent lifecycle coverage impossible to assess.

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