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AWS Pricing MCP Server

by sebdroid

get_pricing_attribute_values

Read-only

Discover valid values for AWS pricing filter attributes, such as instance types or locations, to build precise pricing queries. Specify service code and attribute names, with optional region and regex filters.

Instructions

Get valid values for pricing filter attributes.

**PURPOSE:** Discover what values are available for specific pricing filter attributes of an AWS service.

**WORKFLOW:** Use this after get_pricing_service_attributes() to see valid values for each filter attribute.

**PARAMETERS:**
- Service code from get_pricing_service_codes() (e.g., 'AmazonEC2', 'AmazonRDS')
- region (optional): AWS region (e.g., 'us-east-1', 'eu-west-2'). Defaults to AWS_REGION env var. Set this to match the region you plan to query with get_pricing().
- List of attribute names from get_pricing_service_attributes() (e.g., ['instanceType', 'location'])
- filters (optional): Dictionary mapping attribute names to regex patterns (e.g., {'instanceType': 't3'})

**RETURNS:** Dictionary mapping attribute names to their valid values. Filtered attributes return only matching values, unfiltered attributes return all values.

**EXAMPLE RETURN:**
```
{
    'instanceType': ['t2.micro', 't3.medium', 'm5.large', ...],
    'location': ['US East (N. Virginia)', 'EU (London)', ...]
}
```

**NEXT STEPS:** Use these values in get_pricing() filters to get specific pricing data.

**ERROR HANDLING:** Uses "all-or-nothing" approach - if any attribute fails, the entire operation fails.

**EXAMPLES:**
- Single attribute: ['instanceType'] returns {'instanceType': ['t2.micro', 't3.medium', ...]}
- Multiple attributes: ['instanceType', 'location'] returns both mappings
- Partial filtering: filters={'instanceType': 't3'} applies only to instanceType, location returns all values

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNoAWS region (e.g., "us-east-1", "eu-west-2"). Defaults to AWS_REGION env var (us-east-1). Attributes and values can differ by region, so set this to match the region you plan to query with get_pricing().
filtersNoOptional dictionary mapping attribute names to regex patterns for filtering their values (e.g., {"instanceType": "t3", "operatingSystem": "Linux"})
service_codeYesAWS service code (e.g., "AmazonEC2", "AmazonS3", "AmazonES")
attribute_namesYesList of attribute names (e.g., ["instanceType", "location", "storageClass"])

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already convey readOnlyHint=true, so the description only needs to add extra behavioral context. It does so by disclosing the 'all-or-nothing' error handling and explaining how filters partially apply (e.g., 'filters applies only to instanceType, location returns all values'). This goes beyond the annotation and is valuable for predicting tool behavior.

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

Conciseness4/5

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

The description is well-structured with clear sections (PURPOSE, WORKFLOW, PARAMETERS, RETURNS, etc.) and front-loads the core purpose. It is somewhat lengthy and repeats some schema content, but every section adds useful context such as error handling and examples, so the length is justified.

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?

Given the tool's moderate complexity, the description covers all essential aspects: purpose, workflow, parameter usage, return format, error handling, and examples. It also includes an example return structure, making it fully self-contained for an agent to understand invocation and expected output.

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

Parameters4/5

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

The schema already provides 100% parameter descriptions, so the baseline is 3. The description adds value by including concrete examples for each parameter, clarifying the 'filters' behavior with regex patterns, and explaining the region default and its importance for matching get_pricing() queries. This enriches the semantics beyond the schema fields.

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 begins with 'Get valid values for pricing filter attributes,' which is a specific verb and resource. It clearly distinguishes from sibling tools by noting it is used after get_pricing_service_attributes() to discover valid values, whereas get_pricing_service_attributes returns attribute names themselves.

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 provides explicit workflow context: 'Use this after get_pricing_service_attributes()' and 'Use these values in get_pricing() filters.' It also advises setting the region to match the region used in get_pricing(). However, it does not explicitly state when not to use this tool or mention alternative tools for different scenarios, so it lacks a full when/when-not structure.

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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