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mcp-aws-cost-anomaly

mcp-aws-cost-anomaly

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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables users to analyze AWS cloud costs, audit for wasted or unused resources, and check budget status across multiple profiles and regions through natural language in an MCP-compatible AI assistant. Credentials remain local, with the server using existing AWS CLI configuration to make read-only API calls directly to AWS.
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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables users to analyze AWS cloud costs, audit for wasted resources like stopped EC2 instances and unattached EBS volumes, and check budget status through natural language in any MCP-compatible AI assistant. Supports multi-profile and multi-region querying while keeping AWS credentials local to the user's machine.
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    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables analysis of AWS costs and usage through the AWS Cost Explorer API, supporting multi-account architectures with role assumption and cost comparison, forecasting, and anomaly detection.
      Apache 2.0
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables users and agents to query and analyze AWS spending and Amazon Bedrock model usage through natural-language interactions via MCP. It provides tools for service, EC2, region, and Bedrock usage/cost breakdowns, with optional cross-account retrieval.
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    • F
      license
      Not graded
      quality
      A
      maintenance
      This MCP server enables users to analyze AWS cloud costs, audit for wasted resources, and monitor budgets across multiple profiles and regions. It provides secure, local access to AWS financial data using natural language, with credentials never leaving the machine.
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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables users to query AWS cloud costs, run waste and savings audits, and check budget status in natural language across multiple accounts, profiles, and regions. Runs locally using existing AWS CLI credentials, keeping them on the user's machine while connecting to MCP-compatible AI clients.
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