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rcarmo

office-document-mcp-server

by rcarmo

azure_fetch_prices

Read-only

Fetch and cache Azure retail prices for specified services and regions. Pricing data is stored locally for 24 hours for fast repeated queries.

Instructions

Fetch and cache Azure retail prices for specified services and regions.

Downloads pricing data from the Azure Retail Prices API and stores it locally for fast subsequent queries. Data is cached on disk for 24 hours.

Example: azure_fetch_prices( services=["Virtual Machines", "Azure Databricks", "Storage"], regions=["westeurope", "eastus"] )

azure_fetch_prices(services=["API Management"], force_refresh=True)

Args: services: List of Azure service names to fetch pricing for. If None, fetches common services. regions: List of ARM region names (e.g., "westeurope", "eastus"). If None, fetches common regions. currency: Currency code (default: "USD") force_refresh: If True, ignore cache and fetch fresh data

Returns: Dictionary with fetch status and summary statistics

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
servicesNoList of Azure service names to fetch pricing for. If None, fetches common services.
regionsNoList of ARM region names (e.g., "westeurope", "eastus"). If None, fetches common regions.
currencyNoCurrency code (default: "USD")
force_refreshNoIf True, ignore cache and fetch fresh data
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds details about local caching for 24 hours, force_refresh behavior, and default fallback for services/regions, which are useful beyond annotations.

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?

Well-structured with example and sections, but slightly verbose. The core purpose is clear in the first sentence, and the example is helpful.

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

Completeness4/5

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

With 4 parameters and no output schema, the description covers caching, return type (dictionary with status and summary), and examples. Lacks details on specific return keys and error handling, but overall sufficient.

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%, so baseline is 3. The description repeats parameter descriptions already in the schema but adds no additional meaning beyond what is already provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Fetch and cache Azure retail prices' and mentions the API and caching. It is specific about what it does but does not explicitly distinguish from sibling tools like azure_query_prices.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like azure_query_prices or when to use force_refresh. Examples show usage but lack conditions or exclusions.

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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