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Arshu200

AWS Pricing MCP Server

by Arshu200

generate_cost_report

Generate detailed AWS cost analysis reports with unit pricing, cost breakdowns, and assumptions. Output as markdown or CSV for informed budgeting.

Instructions

Generate a detailed cost analysis report based on pricing data for one or more AWS services.

This tool requires AWS pricing data and provides options for adding detailed cost information.

IMPORTANT REQUIREMENTS:

  • ALWAYS include detailed unit pricing information (e.g., "$0.0008 per 1K input tokens")

  • ALWAYS show calculation breakdowns (unit price × usage = total cost)

  • ALWAYS specify the pricing model (e.g., "ON DEMAND")

  • ALWAYS list all assumptions and exclusions explicitly

Output Format Options:

  • 'markdown' (default): Generates a well-formatted markdown report

  • 'csv': Generates a CSV format report with sections for service information, unit pricing, cost calculations, etc.

Example usage:

{
  // Required parameters
  "pricing_data": {
    // This should contain pricing data retrieved from get_pricing
    "status": "success",
    "service_name": "bedrock",
    "data": "... pricing information ...",
    "message": "Retrieved pricing for bedrock from AWS Pricing url"
  },
  "service_name": "Amazon Bedrock",

  // Core parameters (commonly used)
  "related_services": ["Lambda", "S3"],
  "pricing_model": "ON DEMAND",
  "assumptions": [
    "Standard ON DEMAND pricing model",
    "No caching or optimization applied",
    "Average request size of 4KB"
  ],
  "exclusions": [
    "Data transfer costs between regions",
    "Custom model training costs",
    "Development and maintenance costs"
  ],
  "output_file": "cost_analysis_report.md",  // or "cost_analysis_report.csv" for CSV format
  "format": "markdown",  // or "csv" for CSV format

  // Advanced parameter for complex scenarios
  "detailed_cost_data": {
    "services": {
      "Amazon Bedrock Foundation Models": {
        "usage": "Processing 1M input tokens and 500K output tokens with Claude 3.5 Haiku",
        "estimated_cost": "$80.00",
        "free_tier_info": "No free tier for Bedrock foundation models",
        "unit_pricing": {
          "input_tokens": "$0.0008 per 1K tokens",
          "output_tokens": "$0.0016 per 1K tokens"
        },
        "usage_quantities": {
          "input_tokens": "1,000,000 tokens",
          "output_tokens": "500,000 tokens"
        },
        "calculation_details": "$0.0008/1K × 1,000K input tokens + $0.0016/1K × 500K output tokens = $80.00"
      },
      "AWS Lambda": {
        "usage": "6,000 requests per month with 512 MB memory",
        "estimated_cost": "$0.38",
        "free_tier_info": "First 12 months: 1M requests/month free",
        "unit_pricing": {
          "requests": "$0.20 per 1M requests",
          "compute": "$0.0000166667 per GB-second"
        },
        "usage_quantities": {
          "requests": "6,000 requests",
          "compute": "6,000 requests × 1s × 0.5GB = 3,000 GB-seconds"
        },
        "calculation_details": "$0.20/1M × 0.006M requests + $0.0000166667 × 3,000 GB-seconds = $0.38"
      }
    }
  },

  // Recommendations parameter - can be provided directly or generated
  "recommendations": {
    "immediate": [
      "Optimize prompt engineering to reduce token usage for Claude 3.5 Haiku",
      "Configure Knowledge Base OCUs based on actual query patterns",
      "Implement response caching for common queries to reduce token usage"
    ],
    "best_practices": [
      "Monitor OCU utilization metrics and adjust capacity as needed",
      "Use prompt caching for repeated context across API calls",
      "Consider provisioned throughput for predictable workloads"
    ]
  }
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format ("markdown" or "csv")markdown
exclusionsNoList of items excluded from cost analysis
assumptionsNoList of assumptions for cost analysis
output_fileNoPath to save the report file
pricing_dataYesRaw pricing data from AWS pricing tools
service_nameYesName of the AWS service
pricing_modelNoPricing model (e.g., "ON DEMAND", "Reserved")ON DEMAND
recommendationsNoDirect recommendations or guidance for generation
related_servicesNoList of related AWS services
detailed_cost_dataNoDetailed cost information for complex scenarios
Behavior3/5

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

With no annotations, the description carries the burden of disclosure. It does reveal important behaviors: generates markdown/CSV output, mandates inclusion of unit pricing, calculations, pricing model, and assumptions/exclusions. However, it does not explain how the report is returned (file write vs. string output), what happens with invalid pricing_data, or any side effects. This leaves gaps in understanding the tool's runtime behavior.

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

Conciseness3/5

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

The description is well-structured with headings and bullet points, but it is quite lengthy, especially the example JSON. While informative, it could be more concise by trimming redundant statements like 'This tool requires AWS pricing data' since it is already implied by the first sentence and example. The example is valuable but adds bulk.

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

Completeness3/5

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

Given the tool complexity (10 params, nested objects) and lack of output schema, the description does not fully clarify the return format or behavior when output_file is not provided. It describes the report structure in general terms but leaves ambiguity about whether the tool returns the report as a string or saves to a file. More explicit mention of output delivery would improve completeness.

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

Parameters5/5

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

Although schema description coverage is 100%, the description adds significant value through extensive example usage. It shows the exact structure for pricing_data, detailed_cost_data, and recommendations, which goes far beyond the schema's minimal descriptions. This is crucial for correctly constructing complex nested parameters.

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 the tool generates a detailed cost analysis report for AWS services, which is a specific action on a specific resource. It distinguishes itself from sibling tools like get_pricing (raw data retrieval) and analyze_cdk_project/analyze_terraform_project (infrastructure analysis) by focusing on report generation from pricing data.

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

Usage Guidelines3/5

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

The description notes that the tool 'requires AWS pricing data', implying it should be used after get_pricing, but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. The example shows pricing_data from get_pricing, giving implicit context, but lacks a direct 'use this when...' statement.

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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