azure_sku_discovery

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'intelligent name matching,' which hints at fuzzy search behavior, but lacks details on permissions, rate limits, error handling, or output format. For a discovery tool with no annotation coverage, this is a significant gap in transparency about how the tool operates and what to expect.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates the tool's function and key feature ('intelligent name matching'), making it easy to parse and understand quickly. There's no wasted information or redundancy.

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

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

Given the complexity of Azure SKU discovery, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., list of SKUs with details), potential limitations, or how 'intelligent name matching' works in practice. For a tool with four parameters and no structured output guidance, more context is needed to ensure effective use by an AI agent.

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

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

The input schema has 100% description coverage, providing clear details for all four parameters (e.g., 'service_hint' with fuzzy matching, optional 'region' filtering, defaults for 'currency_code' and 'limit'). The description adds minimal value beyond this, only implying the 'service_hint' parameter through 'intelligent name matching.' With high schema coverage, the baseline score of 3 is appropriate as the schema does most of the work.

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

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

The description clearly states the tool's purpose: 'Discover available SKUs for Azure services with intelligent name matching.' It specifies the verb ('discover'), resource ('SKUs for Azure services'), and a key capability ('intelligent name matching'). However, it doesn't explicitly differentiate from sibling tools like 'azure_discover_skus' or 'azure_price_search,' which appears to be a similar tool, leaving some ambiguity about uniqueness.

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

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

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'azure_discover_skus' and 'azure_price_search' that might overlap in functionality, there's no indication of specific contexts, prerequisites, or exclusions. This lack of differentiation could lead to confusion for an AI agent in selecting the appropriate tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.