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Biosamples

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

TDQS

A4.1/5.0
Behavior4/5

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

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds detail on fetching the page, extracting content, and output format, which complements annotations without contradiction.

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 a single, front-loaded paragraph without wasted words. It efficiently conveys purpose, process, and use cases. Minor improvement could be bullet points for clarity.

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?

Given the tool's simplicity, the description covers purpose, process, output, and use cases. No output schema exists, but annotations compensate. Complete enough for an AI agent.

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?

Both parameters have schema descriptions (100% coverage). The description does not add further meaning beyond what the schema provides, so baseline score applies.

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 generates a production-ready llms.txt file, specifies the process (fetch, extract, emit), and includes distinct use cases. It differentiates from siblings by its unique function.

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 (client indexing, own project, competitor audit) but does not mention when not to use or alternatives. Given no direct siblings, this is sufficient.

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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