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Bioregistry

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

TDQS

A5/5.0
Behavior5/5

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

Discloses embedding model (BGE-base-en), window size (500-char overlapping), truncation behavior (200K cap with flag), and consistency with idempotent/readOnly 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?

A single, front-loaded paragraph with every sentence adding value: purpose, use case, output, technical details, and limitations. No redundancy.

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 sufficiently explains return format (passages with offsets and scores), truncation behavior, and pairing context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds concrete examples (SEC 10-K, article, supply-chain risk) beyond the already descriptive schema, enhancing agent comprehension of parameter usage.

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 a specific verb ('search') and resource ('inside a fetched record'), and differentiates from siblings by specifying it operates on already-fetched text and mentioning pairing with ask_pipeworx_grounded.

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?

Explicitly tells when to use (when record is too big for prompt) and what not to use (avoid cramming full text), and provides an alternative pairing suggestion.

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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