Vultr MCP

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

No annotations are provided, so the description carries full burden. It describes a read-only analysis function ('analyze costs and provide optimization recommendations'), which implies non-destructive behavior, but doesn't disclose any behavioral traits like authentication requirements, rate limits, data freshness, or whether it triggers any side effects. For a cost analysis tool with zero annotation coverage, this leaves significant gaps.

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 with clear sections for purpose, arguments, and return values. It's appropriately sized at 4 sentences plus bullet points, with no redundant information. The front-loaded purpose statement is effective, though the bullet points could be slightly more concise.

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 (cost analysis with optimization recommendations), no annotations, no output schema, and 0% schema coverage, the description provides adequate but incomplete coverage. It documents the parameter and return structure but lacks behavioral context, error conditions, and detailed usage scenarios that would be helpful for 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 includes an 'Args' section that documents the single parameter 'subscription_id' as 'The inference subscription ID or label', adding semantic meaning beyond the schema's bare type definition. With 0% schema description coverage and only 1 parameter, this adequately compensates, though it doesn't provide format examples or validation rules.

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 costs and provide optimization recommendations for an inference subscription.' It specifies the verb ('analyze' and 'provide optimization recommendations'), resource ('inference subscription'), and scope ('costs'). However, it doesn't explicitly distinguish from sibling tools like 'analyze_costs' or 'analyze_inference_usage', which could cause confusion.

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 sibling tools like 'analyze_costs', 'analyze_inference_usage', and 'compare_plans', there's no indication of when this specific optimization tool is appropriate versus general analysis tools. No prerequisites, exclusions, or comparative context are mentioned.

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