datasetShow full metadata for one Ukraine Open Data dataset by id or name (from search_datasets), including all resources and their resource_ids.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Dataset id or name (from search_datasets). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the behavioral safety profile. The description adds value by detailing return contents (full metadata, resources, resource_ids) but does not disclose additional behaviors beyond what annotations provide.
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?
The description is a single, well-structured sentence with no unnecessary words. It front-loads the action and provides all essential information concisely.
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?
For a simple one-parameter tool with comprehensive annotations and no output schema, the description is complete. It covers the purpose, input, and output, and fits well within the sibling toolset.
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 the parameter description already states 'Dataset id or name (from search_datasets)'. The description repeats this without adding extra syntax or format details, meeting the baseline but not exceeding it.
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 verb ('Show full metadata'), the resource ('Ukraine Open Data dataset'), and distinguishes it from sibling search_datasets by indicating retrieval by id or name from search results.
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 implicitly guides usage by mentioning 'from search_datasets', implying a prerequisite. It does not explicitly state when not to use or name alternatives, but the context is clear enough for an agent to understand the workflow.
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.
Most tools have clearly distinct purposes, though the three ask_pipeworx variants (standard, beta, grounded) are very similar, potentially causing confusion. The Polymarket and memory tool families are well-differentiated.
All tool names use snake_case consistently, with a clear verb_noun pattern (e.g., resolve_entity, search_datasets, subscribe). No mixing of conventions.
34 tools is high but justified given the breadth of the Pipeworx platform and Ukraine Open Data integration. The set covers a wide range of data sources and operations without feeling bloated.
The tool surface is thorough, covering querying, comparison, profiling, subscriptions, and memory. Minor redundancy in ask_pipeworx variants, but no significant gaps for the stated data-access purpose.