query_resourcePull rows from a tabular Ukraine Open Data resource (CKAN DataStore) by resource_id (from a dataset's resources). Optional full-text q, limit, offset. Note: only resources with the DataStore enabled are queryable.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Optional full-text filter over the rows. | |
| limit | No | Max rows (default 100). | |
| offset | No | Pagination offset. | |
| resource_id | Yes | A resource_id from dataset/search_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds value by noting the DataStore dependency, which is a behavioral constraint not captured in annotations. No contradictions are present.
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 extremely concise: two sentences and a note. It front-loads the main action and includes only essential information. No wasted words.
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 tool's simplicity (4 parameters, no output schema, safety annotations), the description covers the core functionality, required parameter, optional filters, and a key constraint. It does not explicitly mention the return format, but it is implied by 'Pull rows'. Overall, it is fairly complete for a read-only query tool.
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 description coverage is 100%, so all parameters are documented. The description adds context by specifying that 'resource_id' comes from 'a dataset's resources', linking it to sibling tool outputs. This enhances understanding beyond the bare 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 action 'Pull rows' and the specific resource type 'tabular Ukraine Open Data resource (CKAN DataStore)'. It also specifies the key parameter 'resource_id'. While it doesn't explicitly differentiate from sibling tools like 'search_datasets', it provides enough specificity to distinguish its purpose.
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 includes a note about prerequisites ('only resources with the DataStore enabled are queryable'), which gives a usage condition. However, it does not provide explicit guidance on when to use this tool versus siblings (e.g., 'search_datasets' to find resources, then this to query), leaving the agent to infer 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.