ZenML MCP Server

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 key behaviors: it's a read operation (implied by 'List'), returns JSON with pagination metadata, and mentions the workspace scope. It doesn't cover aspects like rate limits, authentication needs, or error handling, but provides solid foundational context for a listing tool.

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 purpose first, then context, then return format, and finally parameter details. It's appropriately sized for an 11-parameter tool. Minor improvements could include briefer parameter explanations or better formatting, but overall it's efficient with minimal wasted text.

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 complexity (11 parameters, no annotations, but with output schema), the description is quite complete. It covers purpose, context, return format, and all parameters. The output schema existence means return values don't need explanation. It lacks details on error cases or workspace permissions, but provides sufficient information for effective use.

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 compensate fully. It does this excellently by listing all 11 parameters with brief explanations of what each does (e.g., 'Filter by creation date' for 'created', 'The page number to return' for 'page'). This adds substantial meaning beyond the bare schema, making parameter purposes clear.

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 pipeline builds in the ZenML workspace.' It specifies the verb ('List') and resource ('pipeline builds'), and provides additional context about what builds represent ('explain reproducibility...'). However, it doesn't explicitly differentiate this tool from sibling list tools like list_pipelines or list_deployments, which prevents 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 implies usage context by explaining that builds 'can help debug infrastructure issues,' suggesting when this tool might be useful. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like get_build (for single builds) or other list_* tools, nor does it mention any prerequisites or exclusions.

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