ZenML MCP Server

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 states the tool lists secrets but doesn't explain key behaviors like pagination handling (implied by 'page' and 'size' parameters), sorting defaults, or whether it's a read-only operation. For a tool with 7 parameters and no annotations, this leaves significant gaps in understanding how it behaves.

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 front-loaded with the main purpose in the first sentence, followed by a structured 'Args' section. It's efficient and avoids unnecessary fluff. However, the 'Args' section could be more concise by grouping related parameters or using bullet points, slightly affecting readability.

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 has 7 parameters, no annotations, and an output schema exists (which means return values are documented elsewhere), the description is moderately complete. It covers the purpose and parameters but lacks behavioral details (e.g., error handling, rate limits) and usage context. For a list tool with filtering capabilities, more guidance on parameter interactions would be helpful.

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 lists all 7 parameters with brief explanations, adding meaning beyond the input schema (which has 0% description coverage). This compensates well for the schema's lack of descriptions, though it doesn't provide detailed examples or constraints (e.g., format for 'created' or 'updated'). Given the schema coverage is low, this is a strong effort.

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: 'List all secrets in the ZenML workspace.' It specifies the verb ('List') and resource ('secrets in the ZenML workspace'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'list_artifacts' or 'list_models' beyond the resource type, which keeps it from 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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, such as authentication or workspace context, or compare it to other list tools (e.g., 'list_artifacts'). Without this context, users might struggle to choose the right tool for their needs.

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