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 mentions 'Smart identifier resolution' and the return content ('Performance analysis including cache hit ratio, bandwidth usage, and recommendations'), but fails to cover critical aspects like whether this is a read-only operation, if it requires specific permissions, rate limits, or how the analysis is generated (e.g., real-time vs. historical). For a tool with no annotations, this is insufficient.

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, followed by clear sections for 'Args' and 'Returns.' Every sentence adds value without redundancy, making it efficient for an AI agent to parse and understand quickly.

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 parameters, no annotations, no output schema), the description is partially complete. It covers the purpose, parameters, and return content adequately, but lacks details on behavioral traits (e.g., safety, permissions) and doesn't fully explain the analysis methodology or output format, leaving room for improvement in guiding 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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'zone_identifier' accepts 'origin domain, CDN domain, or UUID' and that 'days' has a default of 7 with context ('Number of days to analyze'). This compensates well for the schema's lack of documentation, though it doesn't detail constraints like valid ranges for 'days'.

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: 'Analyze CDN zone performance over the specified period.' It specifies the verb ('analyze'), resource ('CDN zone performance'), and scope ('over the specified period'). However, it doesn't explicitly differentiate from sibling tools like 'get_cdn_zone_stats' or 'monitor_usage', which might offer similar functionality, preventing a perfect 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 some implied usage context by mentioning 'Smart identifier resolution: use origin domain, CDN domain, or UUID,' which suggests when to use this tool for identifier flexibility. However, it lacks explicit guidance on when to choose this tool over alternatives like 'get_cdn_zone_stats' or 'analyze_costs,' and doesn't mention prerequisites or exclusions, leaving gaps for an AI agent.

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