Ludus FastMCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it retrieves a configuration that can be used directly or customized, specifies the return type ('Complete VM configuration dictionary ready for use'), and provides examples of usage. It does not mention error handling, performance, or authentication needs, but covers core operational aspects adequately given 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 appropriately sized and front-loaded: it starts with a clear purpose statement, followed by usage notes, parameter explanations, return value, available skeletons list, and examples. Every sentence adds value, with no wasted words, and the structure guides the user from general to specific information efficiently.

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 (2 parameters, no output schema, no annotations), the description is complete: it covers purpose, usage, parameters with examples, return value, and available options. The lack of output schema is compensated by specifying the return type. It provides all necessary context for an AI agent to understand and invoke the tool correctly without gaps.

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?

Schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the schema: it explains that 'name' is a skeleton name with examples (e.g., 'dc-2022', 'kali'), and that 'customizations' is an optional dict for overriding fields with examples (e.g., {'hostname': 'mydc', 'ram_gb': 8}). This provides clear semantics and usage context for both parameters.

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 with a specific verb ('Get', 'Retrieves') and resource ('VM skeleton template configuration'), and distinguishes it from siblings by specifying it retrieves pre-configured templates rather than building or managing ranges. It explicitly differentiates from tools like 'build_range_from_skeleton' or 'list_vm_skeletons' by focusing on retrieval of a specific skeleton.

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 clear context for when to use this tool: to retrieve a pre-configured VM skeleton for use in range configuration or customization. It implies usage by listing available skeletons and providing examples. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as 'list_vm_skeletons' for listing available skeletons instead of retrieving one.

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