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An AWS waste scanner should retrieve only the billing data it needs, calculate totals and comparisons in deterministic server-side tools, and show the model the numbers with their units and caveats. The model can then explain the findings; it should not be asked to perform hidden arithmetic over a dump of raw billing records. That design is useful, but not novel simply because it uses MCP: AWS announced its own Billing and Cost Management MCP server in August 2025, including a dedicated SQL-based calculation engine.
What the scanner should—and should not—claim
AWS billing data can reveal unusual costs or potential optimization opportunities. It does not, by itself, prove that a resource is waste. A higher bill could reflect increased workload, a changed price, a one-time event, or genuinely idle capacity. “Waste” therefore needs an explicit operational definition and, for consequential recommendations, a review step.
The distinctive value of a scanner is its documented rules: which signals it examines, what thresholds it applies, how it accounts for units and time windows, and how a person can inspect or reject a finding. No particular waste-detection heuristic or realized savings is established by the sources cited here.
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AWS Cost Explorer provides programmatic cost and usage data, from aggregate views down to more granular queries. Its GetCostAndUsage operation accepts selected metrics, filters, groupings, and a time range. The scanner should make those choices explicit rather than requesting broad billing data and relying on a model to infer what matters. See the GetCostAndUsage API reference.
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For example, a user might ask for a month-over-month comparison for one linked account and a selected set of services. The application should translate that request into a bounded time range, a permitted metric such as unblended cost, and deliberate grouping dimensions. The resulting query should be the smallest one that can answer the question.
A narrow, reproducible calculation pipeline
- Accept a bounded scope. Require a time window and account or service scope. Reject ambiguous or unsupported ranges instead of silently substituting defaults.
- Validate metrics and dimensions. Check that the requested Cost Explorer metric, filters, and grouping dimensions are supported for the query. Do not combine values whose units or meanings differ.
- Retrieve only needed data. Apply filters and paginate through all returned pages. AWS advises refining queries and accounting for paginated requests; a scanner should not issue a fresh Cost Explorer request for every conversational turn or user view.
- Normalize results. Keep currency, units, service labels, and time boundaries attached to values. Treat missing, delayed, or differently grouped records as explicit conditions, not as zero.
- Calculate in code. Compute totals, absolute and percentage deltas, and any defined unit-cost measures in server-side functions. For each calculation, preserve the input values, formula, unit, period, and relevant caveats.
- Return evidence for narration. Give the model structured findings and their calculation details. Ask it to explain what the figures may indicate, distinguish observation from inference, and avoid claiming that a cost increase proves waste.
As AWS puts it in its August 22, 2025 announcement of its Billing and Cost Management MCP server, it provides “a dedicated SQL-based calculation engine allowing AI assistants to perform reliable, reproducible calculations.” That makes clear why deterministic calculation through tools is not unique to a proposed scanner; its case rests on the scanner’s particular rules and review workflow.
Keep units and comparisons meaningful
Cost and usage metrics are not interchangeable. AWS warns that summing UsageQuantity across services can be meaningless when the underlying units differ—for example, compute hours and data-transfer gigabytes. A useful scanner either compares like with like or declines to produce a combined total. If it calculates unit cost, it must define both the cost numerator and the matching usage denominator.
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Time comparisons also need a clear basis. Compare equivalent periods and disclose whether the current period is partial. A percentage change should include its baseline and formula; if the baseline is zero or unavailable, report that the percentage cannot be calculated rather than inventing one.
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Freshness, request charges, and caching
Cost Explorer data is not real time. AWS’s API best-practices documentation says billing information is updated up to three times daily. Separately, AWS’s Cost Explorer service overview says updates occur at least every 24 hours and that current-month data becomes available about 24 hours after Cost Explorer is enabled. These statements describe service freshness, not a guarantee that every account’s newest activity will already appear in a particular query. A scanner should show the queried period and avoid presenting recent data as a live meter.
Cost Explorer API usage also has a direct charge. AWS’s pricing page lists $0.01 per request using the primary billing view; requests using custom billing views are priced at $0.01 per source per request. Hourly granularity features have a 14-day lookback and may incur usage-record charges. Pricing and feature details can change, so check the AWS Cost Explorer pricing page for current terms.
Because calls are chargeable and paginated queries can require multiple requests, cache results where appropriate, scope queries tightly, and reuse a result only when its account, filters, metric, grouping, and time range match the new request. AWS’s Cost Explorer API best practices specifically recommend narrowing queries and caching application results.
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Permissions and caller identity
Billing data should be accessed under controlled AWS permissions. AWS recommends a unique role for each user who needs access. The AWS Labs MCP server documentation says its calls use the caller’s AWS credentials and remain subject to AWS service limits and quotas. A scanner should preserve that identity boundary, request only the required permissions, and make clear which account and role supplied the data.
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Do not treat a conversational interface as an authorization layer. Validate access before executing a tool call, keep credentials out of prompts and model-visible output, and ensure that cached results cannot be served across users or accounts without an explicit authorization check.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How this differs from AWS’s MCP server
AWS’s Billing and Cost Management MCP server is a direct comparator, not evidence that MCP alone makes billing analysis reliable. AWS’s announcement describes its SQL-based calculation engine, while the AWS Labs documentation describes the server’s available operations and credential behavior. The proposed waste scanner would need to add a transparent waste definition, signal thresholds, contextual explanations, and a human review path to make a distinct contribution. Its detection quality cannot be inferred merely from access to Cost Explorer or from using MCP.
For large results, the AWS Labs documentation mentions session SQL; because implementation details and availability can change, consult the current AWS Labs Cost Explorer MCP server documentation before relying on that capability.
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Practical checks before acting on a finding
- Confirm the AWS account, billing view, metric, filters, grouping, and period used for the result.
- Check that compared values use compatible units and equivalent time windows.
- Inspect whether the period is incomplete or billing data may not yet include the latest activity.
- Review the rule and threshold that labeled a resource or pattern as potential waste.
- Validate the operational context with the resource owner before changing or removing anything.
- Keep a record of the returned inputs and calculation so the finding can be reproduced later.
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