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sebdroid

AWS Pricing MCP Server

by sebdroid

get_price_list_urls

Read-only

Get direct download URLs for complete AWS pricing datasets, enabling historical pricing analysis and bulk data processing in CSV or JSON formats.

Instructions

Get download URLs for bulk pricing data files.

**PURPOSE:** Access complete AWS pricing datasets as downloadable files for historical analysis and bulk processing.

**WORKFLOW:** Use this for historical pricing analysis or bulk data processing when current pricing from get_pricing() isn't sufficient.

**PARAMETERS:**
- Service code from get_pricing_service_codes() (e.g., 'AmazonEC2', 'AmazonS3')
- AWS region (e.g., 'us-east-1', 'eu-west-1')
- Optional: effective_date for historical pricing (default: current date)

**RETURNS:** Dictionary with download URLs for different formats:
- 'csv': Direct download URL for CSV format
- 'json': Direct download URL for JSON format

**USE CASES:**
- Historical pricing analysis (get_pricing() only provides current pricing)
- Bulk data processing without repeated API calls
- Offline analysis of complete pricing datasets
- Savings Plans analysis across services

**FILE PROCESSING:**
- CSV files: Lines 1-5 are metadata, Line 6 contains headers, Line 7+ contains pricing data
- Use `tail -n +7 pricing.csv | grep "t3.medium"` to filter data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionYesAWS region (e.g., "us-east-1", "eu-west-1")
service_codeYesAWS service code (e.g., "AmazonEC2", "AmazonS3", "AmazonES")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations provide readOnlyHint=true, and the description aligns with that while adding substantial behavioral context: return format (dictionary with 'csv' and 'json' keys), default effective_date behavior, and CSV file processing quirks (metadata lines, header location, grep example). However, it mentions an 'optional effective_date' parameter that does not appear in the schema, which slightly undermines accuracy.

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?

Well-organized with clear headings (PURPOSE, WORKFLOW, PARAMETERS, RETURNS, USE CASES, FILE PROCESSING) and front-loaded purpose. Every section delivers actionable information without fluff, making the length appropriate for the tool's complexity.

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

Completeness5/5

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

Description covers purpose, workflow alternatives, parameter usage, return structure, use cases, and file processing details. Combined with the output schema and readOnlyHint annotation, it gives an agent everything needed to invoke the tool correctly and process results.

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 already covers both parameters with clear descriptions (100% coverage). The description adds value by pointing to get_pricing_service_codes for valid service codes and providing region examples, but it also documents a non-existent 'effective_date' parameter, creating a mismatch between description and schema. Thus, baseline 3 with a minor penalty for the misleading addition.

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?

Description opens with a specific verb+resource phrase ('Get download URLs for bulk pricing data files'), immediately distinguishing this tool from siblings like get_pricing (current pricing) and the service code listing tools. It clearly identifies the deliverable (download URLs) and the intended scope (bulk/historical data).

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

Usage Guidelines5/5

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

Explicitly states when to use this tool: historical pricing analysis or bulk data processing 'when current pricing from get_pricing() isn't sufficient.' It also names an alternative (get_pricing) and refers to get_pricing_service_codes for valid inputs, providing clear guidance on tool selection.

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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