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Bioregistry

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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, indicating safe, non-destructive behavior. The description adds behavioral details: fetches the page, extracts title/description/key links, emits standard llms.txt markdown. It does not contradict annotations and provides valuable process context.

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?

Description is concise: a single paragraph with front-loaded main purpose, followed by process summary and use cases. Every sentence adds value, no fluff. It efficiently covers what, how, and why.

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 simplicity (2 params, no output schema), the description covers core functionality: input URL, optional max_links, extraction process, output format and location. It does not address error cases or rate limits but is complete enough for typical use.

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?

Schema description coverage is 100%, so baseline is 3. The description does not add parameter details beyond what the schema provides, but the overall context helps. Since the schema already describes both parameters well, the description adds no extra semantic value.

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 clearly states the tool generates a production-ready llms.txt file for any URL, specifying verb (generate), resource (llms.txt file), and scope (for any URL). It distinguishes from siblings like ai_visibility_check and scan_competitor_ai_presence by focusing on the llms.txt file generation.

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?

Description provides explicit use cases: indexing a client's site, drafting for own project, auditing competitor. It implicitly tells when to use it but does not explicitly state when not to use or compare to alternatives. However, the context is clear and the use cases are well-defined.

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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