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Biosamples

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details: returns category-bucketed examples with tool/argument shapes, and clarifies that it is safe and idempotent (no side effects). Slight deduction for not explicitly reiterating idempotency, but annotations cover this.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and usage. It is somewhat lengthy due to listing examples and topics, but every sentence earns its place for an onboarding tool. Could be slightly more structured, but still clear and efficient.

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?

With no output schema, the description fully explains what the tool returns (category-bucketed examples with tool/argument shapes). It covers all necessary context: the optional parameter, the use case, and the source (live catalog). Comprehensive for a simple tool.

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% and already describes the topic parameter. The description adds value by providing examples (finance, pharma, betting) and explaining the effect of omission vs. passing a topic, which helps the agent decide how to invoke the tool.

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 explicitly states that the tool returns category-bucketed example questions with exact tool+argument shapes, and positions it as the onboarding entry point for a new agent. It distinguishes from siblings by advising to use it first when unsure what Pipeworx can do.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear instructions: call with no arguments for full spread or pass a topic to focus. Explicitly states to use it first when onboarding, providing both when-to-use and implied alternatives (e.g., ask_pipeworx after gaining familiarity).

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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