get_datasetFetch a single Dryad dataset by its DOI (e.g. "doi:10.5061/dryad.hx3ffbgjj" or "10.5061/dryad.hx3ffbgjj"). Returns full metadata — title, authors, abstract, keywords, publication date, field of science, license, version — plus a download URL for the dataset files. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | Yes | Dataset DOI, with or without the "doi:" prefix (e.g. "doi:10.5061/dryad.hx3ffbgjj"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds 'Keyless' (no authentication) and details the returned fields, providing 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first defines purpose and input, second describes output. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with one parameter, strong annotations, and a clear description of return values, the description is complete enough for an agent to understand behavior and output without an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already describes the DOI parameter. The description adds specific examples and clarifies the optional 'doi:' prefix, adding marginal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a single Dryad dataset by DOI, returning full metadata and a download URL. It distinguishes from sibling tools like 'search_datasets' which would be for querying multiple datasets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching a specific dataset by DOI, and mentions 'Keyless' indicating no auth needed. It does not explicitly state when not to use or recommend alternatives, but the sibling list includes search_datasets for broader searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.