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 and does well by disclosing key behavioral traits: it explains log availability dependencies (plugin and infrastructure support), specifies default and recommended limits for the 'tail' parameter, and describes the return format (JSON with logs and truncation metadata). It does not cover aspects like rate limits or authentication needs, but provides substantial operational context.

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 the core purpose, followed by usage context, prerequisites, and detailed parameter explanations in a structured format (Args/Returns). Every sentence adds value without redundancy, making it efficient and easy to parse.

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 (retrieving logs with dependencies), no annotations, and an output schema present (which covers return values), the description is complete enough. It addresses purpose, usage, behavioral constraints, and parameter details, leaving no significant gaps for an agent to invoke the tool correctly.

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, which it does by explaining all three parameters: 'name_id_or_prefix' (accepts name, ID, or prefix), 'project' (optional scope with default), and 'tail' (number of lines with default and max recommendation). It adds meaning beyond the bare schema, clarifying usage and constraints effectively.

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 logs') and resource ('for a specific deployment'), distinguishing it from sibling tools like 'get_deployment' (which likely retrieves deployment metadata) and 'get_step_logs' (which targets different logs). It explicitly mentions retrieving logs from underlying infrastructure, providing precise scope.

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 the tool ('debugging deployment issues or monitoring deployment behavior') and notes prerequisites ('deployer plugin being installed' and 'deployment infrastructure supporting log retrieval'). However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'get_step_logs' for pipeline-related logs.

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