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Dryad

by io.github.pipeworx-io

Search Datasets

search_datasets
Read-onlyIdempotent

Search Dryad, a curated repository of open research datasets (mostly the data underlying published, peer-reviewed studies in any field of science). Full-text query over titles, abstracts, authors and keywords. Returns matching datasets with DOI, title, authors, truncated abstract, keywords, publication date, field of science and license. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, 1-based (default 1).
limitNoMax results per page (default 20, max 100).
queryYesSearch terms (e.g. "coral bleaching", "maize genome").

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false, indicating safe, read-only behavior. The description adds that it is keyless (no authentication needed) and mentions 'truncated abstract', providing useful behavioral context beyond 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?

Three sentences, no wasted words. First sentence establishes context (Dryad repository), second explains search capability, third lists return fields. Front-loaded with purpose 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?

Despite lacking an output schema, the description fully explains return fields and search behavior. Combined with strong annotations (read-only, etc.), the tool is completely specified for agent use.

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%, so the schema documents all parameters. The description adds value by specifying that query searches over titles, abstracts, authors, and keywords, and enumerates return fields, which is not in the schema. This helps the agent understand what the tool returns.

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 it searches Dryad, a specific repository of open research datasets from peer-reviewed studies. It explains the full-text query over titles, abstracts, authors, and keywords, and lists exact return fields (DOI, title, authors, etc.), distinguishing it from sibling tools like 'get_dataset'.

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

Usage Guidelines3/5

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

The description implies use for finding datasets in Dryad and notes it is keyless (no auth). However, it does not explicitly state when to use this tool versus alternatives like 'search_within' or 'get_dataset', nor does it provide when-not conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Dryad toolsInspect
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TDQS

A4.1/5.0
Disambiguation3/5

There is notable overlap between tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, as well as between polymarket_edges and bet_research. While descriptions differentiate them, an agent may struggle to pick the right one without deep understanding of nuances.

Naming Consistency3/5

Tool names use mixed conventions: snake_case (ai_visibility_check), verb_noun (ask_pipeworx, bet_research), and single-word verbs (forget, recall). Some names are very long (polymarket_fill_risk) while others are terse, reducing predictability.

Tool Count3/5

33 tools is high but can be justified by the broad domain coverage (data retrieval, entity resolution, comparisons, memory, subscriptions). However, several tools serve similar purposes, suggesting potential consolidation.

Completeness4/5

The tool set covers a wide range of data sources and tasks (SEC, FDA, real estate, prediction markets, etc.) with CRUD-like operations on memory and subscriptions. Minor gaps exist, such as no direct stock trading or social media monitoring, but overall coverage is strong.

Resources