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chaandannn

nable (finops-mcp)

generate_account_dashboard

Generate a cost dashboard for your AWS account showing current vs previous month spend, projected costs, top drivers, optimization chances, and budget status. Opens as a self-contained HTML page.

Instructions

Generate a cost dashboard for the account and open it in your browser.

Shows total spend this month vs last month, projected spend, top cost drivers by service, open optimization opportunities, realized savings, and budget status. Outputs a self-contained HTML file.

Args: account_id: AWS account ID to scope the dashboard. Auto-detected from your configured credentials when omitted. open_browser: Open the HTML file in the default browser (default True). push_to_notion: Also push a summary to your configured Notion page (requires NOTION_API_KEY and NOTION_PAGE_ID env vars).

Use when: - "Show me a dashboard" - "Give me a summary of my costs" - "Generate the account dashboard" - "What does my cost health look like?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idNo
open_browserNo
push_to_notionNo
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explains that the dashboard opens in a browser by default (but can be suppressed via open_browser parameter) and can optionally push a summary to Notion (with env var requirements). It also mentions outputting a self-contained HTML file. No annotations are provided, so the description carries the full burden and does it well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose statement, a list of dashboard contents, explicit parameter descriptions, and a targeted 'Use when' section. Every sentence adds value and there is no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (generating a dashboard with multiple metrics and optional features), the description covers the main functionality, parameters, and prerequisites. It could mention what happens on failure or error states, but overall it is sufficiently complete for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description includes an 'Args' section that explains each parameter: account_id is auto-detected, open_browser defaults to True, push_to_notion requires specific env vars. Since schema description coverage is 0%, the description compensates effectively by adding meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool generates a cost dashboard and opens it in a browser, listing specific metrics included (total spend, projection, top cost drivers, etc.). This distinguishes it from sibling tools like get_cost_summary or get_cost_by_service, which are more narrow in scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a 'Use when' section with explicit phrases like 'Show me a dashboard' and 'Give me a summary of my costs.' It does not explicitly state when not to use it, but the listed use cases are clear and provide good guidance for the agent.

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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