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 returns complete YAML content as a string, specifies that the output is ready to save and deploy with Ludus, and includes an example with deployment commands. However, it lacks details on error handling, rate limits, or authentication needs, which are minor gaps given the tool's simple read-only nature.

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, starting with a clear purpose statement, followed by returns, args, returns, available examples, and an example usage. Each sentence earns its place by providing essential information without redundancy, and the structure guides the user from high-level intent to specific implementation details 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 tool's low complexity (1 parameter, no output schema, no annotations), the description is complete enough. It covers purpose, usage, parameter details, return value, and deployment context, addressing all necessary aspects for a simple retrieval tool. The inclusion of example names and deployment steps adds practical completeness without overloading the description.

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 fully compensate. It does so by clearly explaining the single parameter 'name' as the 'Example name', listing the three valid values ('basic_ad', 'with_siem', 'web_app_lab') with brief descriptions of each, and providing an example usage. This adds significant meaning beyond the bare schema, ensuring the parameter is well-understood.

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') and resource ('YAML configuration example'), and distinguishes it from siblings by focusing on retrieving pre-built YAML examples rather than generating, exporting, or managing configurations like 'export_range_config_to_yaml' or 'generate_config_from_description'. It explicitly lists available examples to reinforce its unique role.

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 explicit usage guidance by stating when to use this tool ('Get a ready-to-use YAML configuration example') and includes an example with deployment instructions, implicitly distinguishing it from alternatives like 'list_yaml_examples' (which likely lists rather than retrieves content) or 'build_range_from_description' (which generates rather than fetches). The context of 'ready-to-use' and direct file-saving hints at its specific use case.

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