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AWS Billing and Cost Management MCP Server

Official AWS Labs MCP server for AWS Billing and Cost Management that lets AI assistants analyze costs and usage, monitor budgets and anomalies, and surface Cost Optimizer, Savings Plans, and Reserved Instance recommendations.

by AWS Labs·added 2026-06-21·
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Review first — review before installing

Open the source and read safety notes before installing.

Citation facts

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Canonical URL
https://heyclau.de/entry/mcp/aws-billing-cost-management-mcp-server
Source URLs
https://github.com/awslabs/mcp/blob/main/src/billing-cost-management-mcp-server/README.md, https://github.com/awslabs/mcp, https://awslabs.github.io/mcp/
Brand
AWS Labs
Brand domain
aws.amazon.com
Brand asset source
brandfetch
Safety notes
The provided tools read cost and usage data and recommendations; they do not purchase Savings Plans or Reserved Instances or otherwise modify your account. Grant least-privilege read access to the cost APIs., This server reads billing data with your AWS credentials; scope the profile to the intended account and run it only on a trusted host., Cost Explorer and some cost APIs charge per paid API request; broad or frequent queries can incur AWS costs, so scope time ranges and granularity deliberately.
Privacy notes
Cost, usage, budget, and account metadata — including linked-account identifiers and spend figures — can be returned to the model., Keep account identifiers, credentials, and spend details out of public prompts, issues, and screenshots, since billing data is sensitive business information.
Author
AWS Labs
Submitted by
jaso0n0818
Claim status
unclaimed
Last verified
2026-06-21

Safety notes

  • The provided tools read cost and usage data and recommendations; they do not purchase Savings Plans or Reserved Instances or otherwise modify your account. Grant least-privilege read access to the cost APIs.
  • This server reads billing data with your AWS credentials; scope the profile to the intended account and run it only on a trusted host.
  • Cost Explorer and some cost APIs charge per paid API request; broad or frequent queries can incur AWS costs, so scope time ranges and granularity deliberately.

Privacy notes

  • Cost, usage, budget, and account metadata — including linked-account identifiers and spend figures — can be returned to the model.
  • Keep account identifiers, credentials, and spend details out of public prompts, issues, and screenshots, since billing data is sensitive business information.

Prerequisites

  • An AWS account with Billing and Cost Management access (Cost Explorer, Budgets, and related APIs enabled).
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) with read permissions for the Cost Explorer, Budgets, Compute Optimizer, and related cost APIs.
  • An MCP client that supports stdio servers; the server runs locally on the same host as the client.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
10 minutes
Difficulty
intermediate
Tool listing metadata
Pricing
open-source
Disclosure
editorial
Application category
DeveloperApplication
Operating system
Cross-platform
Full copyable content
{
  "awslabs.billing-cost-management-mcp-server": {
    "command": "uvx",
    "args": ["awslabs.billing-cost-management-mcp-server@latest"],
    "env": {
      "AWS_PROFILE": "${AWS_PROFILE}",
      "AWS_REGION": "us-east-1",
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}

About this resource

Overview

AWS Billing and Cost Management MCP Server is an official AWS Labs Model Context Protocol server that gives AI assistants access to AWS Billing and Cost Management capabilities. It lets Claude analyze historical and forecasted costs, monitor budgets and anomalies, and surface cost-optimization recommendations through standardized MCP tools.

It runs locally over stdio via uvx from the published awslabs.billing-cost-management-mcp-server Python package and uses your local AWS credentials. All API calls follow AWS service limits and quotas.

Features

  • Cost and usage analysis — Cost Explorer insights with flexible grouping and filtering, plus usage-metric trends.
  • Budgets and anomalies — check budget status against actual spend and detect unusual spending patterns and their root causes.
  • Optimization recommendations — Compute Optimizer right-sizing and Cost Optimization Hub savings opportunities.
  • Savings Plans and Reserved Instances — RI coverage analysis and personalized Savings Plans recommendations.
  • Comparisons and Storage Lens — month-over-month and multi-account cost comparisons, plus S3 Storage Lens metric queries.

Use Cases

  • Break down last month's AWS bill by service, account, or tag.
  • Check whether spending is tracking against budgets and flag anomalies.
  • Compare cost and usage month-over-month to find cost drivers.
  • Surface Savings Plans, Reserved Instance, and right-sizing recommendations.

Installation

Claude Code

  1. Install Python 3.10+ and uv.
  2. Configure an AWS profile with read access to the cost and billing APIs.
  3. Add the server with the stdio configuration above (command uvx, package awslabs.billing-cost-management-mcp-server@latest).
  4. Verify it is connected with claude mcp list.

Claude Desktop / Cursor / Kiro / VS Code

Add the configSnippet above to your client's MCP configuration and set AWS_PROFILE/AWS_REGION. The first run downloads the package via uvx.

Source And Trust

This entry is based on the official AWS Labs awslabs/mcp repository and the published PyPI package (Apache-2.0). The server reads cost and usage data with your AWS credentials and some cost APIs are billed per request, so scope permissions to cost reads, keep spend data private, and verify the configuration against the linked source before using it in automated workflows.

#aws#cost-management#finops#billing#aws-labs

Source citations

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How it compares

AWS Billing and Cost Management MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

Field
AWS Labs logoAWS Billing and Cost Management MCP Server

Official AWS Labs MCP server for AWS Billing and Cost Management that lets AI assistants analyze costs and usage, monitor budgets and anomalies, and surface Cost Optimizer, Savings Plans, and Reserved Instance recommendations.

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BrandAWS Labs logoAWS LabsAWS Labs logoAWS LabsAWS Labs logoAWS LabsAWS Labs logoAWS Labs
Categorymcpmcpmcpmcp
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorAWS LabsAWS LabsAWS LabsAWS Labs
Added2026-06-212026-06-212026-06-212026-06-21
Platforms
Claude CodeCodexCursorClaude Desktop
Claude CodeClaude Desktop
Claude CodeClaude Desktop
Claude CodeClaude Desktop
Source repo————
Safety notes✓The provided tools read cost and usage data and recommendations; they do not purchase Savings Plans or Reserved Instances or otherwise modify your account. Grant least-privilege read access to the cost APIs. This server reads billing data with your AWS credentials; scope the profile to the intended account and run it only on a trusted host. Cost Explorer and some cost APIs charge per paid API request; broad or frequent queries can incur AWS costs, so scope time ranges and granularity deliberately.✓The configuration above is read-only. Adding the `--allow-write` flag lets the server create, update, patch, and delete EKS/Kubernetes resources (including creating clusters via CloudFormation) and `--allow-sensitive-data-access` exposes logs and events; enable these only deliberately. This server acts on real infrastructure with your AWS credentials; scope the profile to the intended account, region, and clusters, and prefer non-production targets while evaluating it. Run it only on a trusted host, and review any generated manifests or CloudFormation actions before applying them.✓Run with the `--readonly` flag (shown above) to block all mutating operations. Without it the server can create and delete IAM users, roles, groups, policies, and access keys — high-impact identity changes — so enable write access only deliberately and with scoped permissions. IAM controls account-wide access; a misused write operation can grant or revoke permissions broadly. Prefer non-production accounts while evaluating, and use policy simulation to test changes before applying them. This server acts on real IAM with your AWS credentials; scope the profile tightly and run it only on a trusted host.✓The configuration above is read-only. Setting `ALLOW_WRITE=true` lets the server create and modify infrastructure (ECR repos, CloudFormation stacks, ECS services) and `ALLOW_SENSITIVE_DATA=true` exposes logs; enable these only deliberately. AWS documents this server as primarily for development, testing, and non-critical environments; keep write/sensitive-data disabled for production accounts and prefer non-production targets while evaluating it. This server acts on real infrastructure with your AWS credentials; scope the profile to the intended account, region, and resources, and run it only on a trusted host.
Privacy notes✓Cost, usage, budget, and account metadata — including linked-account identifiers and spend figures — can be returned to the model. Keep account identifiers, credentials, and spend details out of public prompts, issues, and screenshots, since billing data is sensitive business information.✓Cluster state, resource manifests, ARNs, and account/region metadata can be returned through tool calls and exposed to the model. With sensitive-data access enabled, pod logs and Kubernetes events may be returned; keep account identifiers, credentials, and log contents out of public prompts, issues, and screenshots.✓IAM user/role/group names, ARNs, policy documents, and account metadata can be returned through tool calls and exposed to the model. Access key IDs and other identity material may appear in responses; never expose secret access keys, and keep account identifiers and policy contents out of public prompts, issues, and screenshots.✓Cluster, service, task, task-definition, and ECR metadata plus account/region identifiers can be returned through tool calls and exposed to the model. With sensitive-data access enabled, logs and deployment details may be returned; keep account identifiers, credentials, and log contents out of public prompts, issues, and screenshots.
Prerequisites
  • An AWS account with Billing and Cost Management access (Cost Explorer, Budgets, and related APIs enabled).
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) with read permissions for the Cost Explorer, Budgets, Compute Optimizer, and related cost APIs.
  • An MCP client that supports stdio servers; the server runs locally on the same host as the client.
  • An AWS account with Amazon EKS and permissions to view (and, if enabled, manage) the target clusters.
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) scoped to the intended account, region, and clusters.
  • An MCP client that supports stdio servers; the server runs locally on the same host as the client.
  • An AWS account with IAM access and permissions for the IAM read (and, if write is enabled, manage) operations you intend to use.
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) scoped least-privilege; read-only IAM permissions are enough for the recommended `--readonly` mode.
  • An MCP client that supports stdio servers; the server runs locally on the same host as the client.
  • An AWS account with Amazon ECS/ECR and permissions to view (and, if enabled, deploy) the target resources.
  • Docker or Finch for containerization and local image builds.
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) scoped to the intended account, region, and resources.
Install
uvx awslabs.billing-cost-management-mcp-server@latest
uvx awslabs.eks-mcp-server@latest
uvx awslabs.iam-mcp-server@latest --readonly
uvx --from awslabs-ecs-mcp-server ecs-mcp-server
Config
{
  "mcpServers": {
    "awslabs.billing-cost-management-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.billing-cost-management-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "awslabs.eks-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.eks-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "awslabs.iam-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.iam-mcp-server@latest", "--readonly"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "awslabs.ecs-mcp-server": {
      "command": "uvx",
      "args": ["--from", "awslabs-ecs-mcp-server", "ecs-mcp-server"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR",
        "ALLOW_WRITE": "false",
        "ALLOW_SENSITIVE_DATA": "false"
      },
      "type": "stdio"
    }
  }
}
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