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 states the tool retrieves information, implying a read-only operation, but fails to mention critical details like authentication requirements, rate limits, error conditions (e.g., invalid database_id), or whether the operation is idempotent. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 front-loaded, with the core purpose stated first, followed by parameter and return value sections. It avoids unnecessary verbosity. However, the 'Args' and 'Returns' labels are slightly redundant given the structured schema, and the return description could be more specific, preventing a perfect score.

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 moderate complexity (2 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameters but lacks details on usage context, behavioral traits, error handling, and output structure. Without annotations or an output schema, the agent must infer missing information, making this description incomplete for reliable tool invocation.

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 schema description coverage is 0%, so the description must compensate. It lists the parameters ('database_id' and 'username') and briefly explains their purpose ('The database ID or label' and 'The username to get information for'), adding basic semantics beyond the bare schema. However, it lacks details on parameter formats (e.g., string constraints, examples) or interdependencies, leaving room for ambiguity. This meets the baseline for partial compensation but not fully.

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 information about a specific database user.' It specifies the verb ('Get') and resource ('database user'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'list_users' or 'analyze_user_permissions', which reduces the score from a perfect 5.

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 any mention of prerequisites, such as requiring a specific database ID or username format, or comparisons to siblings like 'list_users' for broader queries or 'analyze_user_permissions' for detailed analysis. This omission leaves the agent without context for tool selection.

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