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Bioregistry

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (idempotent, not destructive), the description adds behavioral context: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous. No contradictions.

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?

Concise, well-structured. Front-loaded with purpose, followed by usage guidance, behavioral notes, and pairing instructions. Every sentence serves a purpose.

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?

Given simple input schema (2 strings), no output schema, and annotations covering idempotent and non-destructive nature, the description fully contextualizes the tool: what it stores, how long, how to retrieve/delete.

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 covers 100% of parameters with clear descriptions. The description adds value by suggesting key naming conventions (e.g., 'subject_property') and specifying value types (findings, addresses).

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 saves data for reuse across conversations or sessions, with specific examples of what to store (ticker, address, preference). It distinguishes itself from sibling tools 'recall' and 'forget' by mentioning pairing with them.

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?

Explicit guidance on when to use ('when you discover something worth carrying forward') and context about persistence (authenticated vs anonymous). It suggests pair tools (recall, forget), making usage 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

A3.5/5.0
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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