List dashboards in your clariBI organization. Returns id, name, status, last refresh, and a URL you can open in a browser.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Page size (1–100). | |
| offset | No | Row offset for pagination. | |
| search | No | Optional substring match against dashboard name. |
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | ||
| limit | Yes | ||
| total | Yes | Total dashboards matching the query. | |
| offset | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description need not repeat safety. It adds value by specifying the exact returned fields and the browser URL, which is useful behavioral context beyond the schema.
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, front-loaded sentence that states the action and output. Every word earns its place, with no redundancy or filler.
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 list tool, the description is complete: it explains what is returned, while the schema covers pagination and search parameters. The output schema and annotations reduce the need for further elaboration.
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%, with clear descriptions for limit, offset, and search. The description does not add parameter details, but the schema already carries the semantic burden, so the baseline of 3 applies.
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 lists dashboards in the clariBI organization, which is a specific verb+resource. It distinguishes from siblings like get_dashboard, list_data_sources, and list_reports by focusing on dashboards and mentioning the returned fields.
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 as a listing tool but provides no explicit guidance on when to use it versus alternatives like get_dashboard for a single dashboard. There is no mention of exclusions, but the context is clear enough for basic selection.
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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Tools are generally distinct by resource and action, but a few status polling tools (check_integration_status, get_analysis_status) could be confused without careful reading; descriptions clarify the difference.
Most tools use a consistent verb_noun snake_case pattern, but there is minor variation (e.g., 'check' vs 'get' for status, and some compound nouns like 'request_oauth_integration_url').
26 tools is slightly above the typical range but appropriate for a comprehensive BI platform covering data ingestion, analysis, forecasting, reports, dashboards, and account management; each tool has a clear purpose.
The tool surface is heavily read-oriented, lacking update and delete operations for most resources (data sources, dashboards, reports, forecasts). This leaves significant lifecycle management gaps for an agent.