Get the organization's current AI credit usage, data source count, user count, and rate-limit headroom.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| max_users | No | ||
| trial_ends_at | No | ||
| ai_credits_used | Yes | ||
| ai_credits_limit | Yes | ||
| storage_gb_limit | No | ||
| data_sources_used | No | ||
| subscription_tier | Yes | ||
| data_sources_limit | No | ||
| subscription_status | Yes | ||
| ai_credits_remaining | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds the specific data points returned (AI credit usage, counts, rate-limit headroom), which gives the agent a clear expectation of the tool's output scope. No contradictions or missing safety notes.
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 that front-loads the action ('Get') and the resource ('organization's current...'). Every word adds value; no filler or redundancy.
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 zero-parameter, read-only tool with an output schema, the description fully captures the tool's scope and the specific data points it returns. There are no apparent gaps in what the agent needs to know.
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?
The tool has zero parameters, making schema coverage trivially 100%. The description does not need to explain parameter semantics, and the baseline for no parameters is 4.
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 identifies the tool's purpose: retrieving organizational usage metrics (AI credit usage, data source count, user count, and rate-limit headroom). The verb 'Get' and the explicit resource scope distinguish it from sibling tools like get_billing_status or get_dashboard.
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?
No explicit when-to-use or alternative comparisons are provided. The purpose is self-evident for a simple read-only getter, but the description does not guide the agent on choosing this tool over similar ones (e.g., get_billing_status). The usage is implied rather than stated.
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.
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.