Vultr MCP

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 describes a read operation ('Get') and details the return structure, which is helpful. However, it omits critical behavioral traits such as authentication requirements, rate limits, error handling, or whether the data is cached. The description adds value by specifying the output format but falls short of fully compensating for the lack of annotations.

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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by dedicated 'Args' and 'Returns' sections that efficiently document inputs and outputs. Every sentence adds value without redundancy, though the formatting as a multi-line string could be slightly optimized for front-loading.

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 tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but has gaps. It covers the purpose, parameter semantics, and return structure, which is sufficient for basic use. However, it lacks context on authentication, error cases, or integration with sibling tools, making it incomplete for robust agent operation in a complex environment.

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 0% description coverage, but the description compensates by explaining the 'app_id' parameter: 'The marketplace application name, short_name, or image_id (e.g., "openlitespeed-wordpress")'. This adds semantic meaning beyond the schema's type constraint, clarifying acceptable values with an example. Since there's only one parameter, this is sufficient for high utility.

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: 'Get configuration variables for a marketplace application.' It specifies the verb ('Get'), resource ('configuration variables'), and scope ('marketplace application'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'get_application' or 'list_marketplace_applications', which might provide related information, so it doesn't reach the highest score.

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. It lacks context about prerequisites (e.g., needing an existing marketplace app), exclusions (e.g., not for non-marketplace apps), or comparisons with sibling tools like 'get_application' that might retrieve different data. This absence leaves the agent without clear usage direction.

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