mcp-cloudron

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

With no annotations provided, the description carries the full burden. It adds useful behavioral context: logs are formatted with timestamps and severity levels for readability, and it specifies the API endpoints. However, it doesn't cover important aspects like rate limits, authentication needs, error handling, or response format, leaving gaps for a tool with no annotation support.

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 with two sentences: the first states the purpose and log formatting, the second explains the type parameter's effect. It's front-loaded with key information and avoids redundancy, though it could be slightly more streamlined by integrating the formatting detail with the endpoint explanation.

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 no annotations and no output schema, the description is incomplete. It covers the basic purpose and some behavioral traits but misses critical details like the response structure (e.g., log format specifics beyond 'timestamps and severity levels'), error cases, or performance considerations. For a 3-parameter tool with no structured support, this leaves significant 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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema: it reiterates the 'type' parameter's role in endpoint selection but doesn't provide additional semantic context (e.g., examples of resourceId formats or practical use cases for lines). Baseline 3 is appropriate as the schema does the heavy lifting.

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 as 'Get logs for an app or service' with a specific verb ('Get') and resource ('logs'), distinguishing it from siblings like cloudron_get_app or cloudron_get_status. However, it doesn't explicitly differentiate from potential log-related siblings (none exist in the provided list), so it's not 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 implies usage by specifying the 'type' parameter determines the endpoint (app vs. service logs), but it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., no mention of other logging tools or when to prefer app over service logs). It offers some context but lacks clear when/when-not directives.

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