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 describes the type of information returned (image info, code embedding, stack checksums) and the context (reproducibility and infrastructure setup), which adds useful context beyond a basic read operation. However, it does not mention potential errors (e.g., if the build ID is invalid), permissions required, or rate limits, leaving gaps in behavioral understanding.

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 context on build contents, and then parameter details in a structured format. 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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is largely complete. It explains the purpose, parameters, and context of build information. Since an output schema exists, it does not need to detail return values, but it could improve by mentioning error cases or usage constraints to be fully comprehensive.

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. It provides clear explanations for all three parameters: 'id_or_prefix' (ID or prefix of the build), 'project' (optional project scope with default), and 'hydrate' (whether to add additional details with default). This adds significant meaning beyond the bare schema, though it could benefit from examples or format details for 'id_or_prefix'.

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 detailed information') and resource ('about a specific pipeline build'), distinguishing it from sibling tools like 'list_builds' which would return multiple builds. It specifies the type of information retrieved (image info, code embedding, stack checksums) and the context (reproducibility and infrastructure setup).

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 mentioning it retrieves details for a 'specific pipeline build,' suggesting it should be used when detailed information about an individual build is needed. However, it does not explicitly state when to use this tool versus alternatives like 'list_builds' or provide any exclusions or prerequisites for usage.

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