List this account's org-shared dashboards — visible to every account member. Does not include any member's PERSONAL dashboard: those live in one specific signed-in user's own browser session and have no id an account-level MCP key can address. Use a returned id with get_dashboard, set_dashboard_widgets or delete_dashboard.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and non-destructive, and the description adds meaningful scope behavior: it lists only org-shared dashboards, excludes personal dashboards, and explains the ID limitation for personal dashboards. This goes beyond the 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?
Three sentences with no filler. The main action and scope are front-loaded, exclusions are explained, and the final sentence routes to related tools. Every sentence contributes necessary 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?
For a parameterless read-only list tool, the description fully covers what is listed, what is excluded, why the exclusion exists, and how to use the results. Nothing needed for correct invocation is missing.
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?
There are zero parameters and the schema documents 100% coverage, so no parameter explanation is needed. The description correctly avoids inventing parameters and instead clarifies the output scope. Baseline 4 is appropriate for a parameterless tool.
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 names a specific action and resource ('List this account's org-shared dashboards') and clearly differentiates it from personal dashboards and downstream tools. The scope is unmistakable and it distinguishes itself from sibling tools like 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?
The description explicitly states what is included and excluded, explains why personal dashboards are not addressable, and tells the agent how to use returned IDs with related tools. This gives clear when-to-use and when-not-to-use guidance.
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 organized by resource (budgets, alerts, anomalies, dashboards, cost tags, recommendations), so most are clearly separable. The cost-tag cluster and the dimension/facet listers are the places where an agent could misselect by name, though descriptions resolve the ambiguity.
All tools use snake_case verb_noun names with a clear convention: get_ fetches specific items, list_ enumerates collections, and query_ runs time-bucketed or analytical queries. The pattern holds across all 29 tools with no camelCase or mixed verb styles.
29 tools is well past the typical 3–15 sweet spot and even past the 16–25 heavy band, so the surface feels sprawling despite having few duplicates. Each tool maps to a distinct endpoint, but the sheer number makes it a heavy set for an agent to select from.
The read-side is strong: costs, usage, tags, budgets, alerts, anomalies, dashboards, recommendations, and data health are all queryable. However, the surface is almost entirely read-only, and descriptions reference absent tools like create_budget, create_alert_subscription, create_dashboard, set_dashboard_widgets, and delete_dashboard, creating dead ends. That is a significant gap for a cost-management platform.