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AWS Pricing MCP Server

AWS Pricing MCP Server

by TheArshadShaik
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

analyze_cdk_project

Eliminate guesswork in cost estimation by identifying AWS services used in a CDK project. Extracts service details directly from CDK constructs, doing away with hardcoded mappings.

Instructions

Analyze a CDK project to identify AWS services used. This tool dynamically extracts service information from CDK constructs without relying on hardcoded service mappings.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_path": "<project_path>"
    },
    "name": "analyze_cdk_project"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_pathYesPath to the project directory

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds a useful non-obvious detail: 'dynamically extracts service information from CDK constructs without relying on hardcoded service mappings.' This explains internal behavior. However, it does not disclose potential side effects (e.g., read-only vs. file modifications), output format, or prerequisites needed for analysis.

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 two sentences: the first states the core purpose, and the second adds a relevant technical detail. It is front-loaded, concise, and contains no redundant or filler content.

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?

For a simple one-parameter tool, the description provides enough to select the tool and know what it does. However, it omits the return value/output format (e.g., list of service names) and any side effects or dependencies. Given the absence of annotations and an output schema, a bit more context would complete the picture.

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

Parameters3/5

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

Schema coverage is 100% with project_path described as 'Path to the project directory.' The tool description does not add any additional parameter semantics beyond what the schema already provides, so the baseline of 3 is appropriate.

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's purpose: 'Analyze a CDK project to identify AWS services used.' It uses a specific verb ('analyze') with a specific resource ('CDK project') and an explicit outcome. It also distinguishes itself from the sibling analyze_terraform_project by specifying CDK, making the tool's scope unambiguous.

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 implies usage for CDK projects, and the second sentence explains the method (dynamic extraction from CDK constructs), giving context. However, it does not explicitly mention when not to use this tool or name alternatives like analyze_terraform_project. The guidance is clear but relies on the sibling name to infer the Terraform alternative.

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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