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ZenML MCP Server

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by zenml-io
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Python
Hybrid

get_step_logs

Retrieve logs for a specific step run using its ID. Optionally filter by log source or exact log record ID.

Instructions

Get the logs for a specific step run.

Args:
    step_run_id: The ID of the step run to get logs for.
    source: Optional log source. Defaults to ZenML's ordinary ``step`` source.
    logs_id: Optional exact log record ID. Cannot be combined with ``source``.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNo
logs_idNo
step_run_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changedv2.0.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / logs_id
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Logs Id"
+}
    • addedInput schema / properties / source
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Source"
+}
    • addedOutput schema / additionalProperties
      Added value: +true
    • removedOutput schema / properties
      Removed value: -{
-  "result": {
-    "title": "Result",
-    "type": "string"
-  }
-}
    • removedOutput schema / required
      Removed value: -[
-  "result"
-]
    • changedOutput schema / title
      Previous value: -"get_step_logsOutput"New value: +"get_step_logsDictOutput"
  2. First observedv1.2.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses the optional source default and the mutual exclusion between source and logs_id, which is useful. It does not mention return format, pagination, or error behavior, though the presence of an output schema mitigates the return-format gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the purpose. The Args section is terse and each line adds specific meaning without filler or redundant restatement of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a focused retrieval tool with an output schema, the description is largely complete: it covers the operation and all parameters. Minor gaps remain around possible source values and behavior when no logs exist, but these are not critical for a typical call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description is the only source of parameter meaning. It explains all three parameters clearly, including the default behavior of source and the exclusivity constraint on logs_id. This fully compensates for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description begins with a specific verb and resource: 'Get the logs for a specific step run.' This clearly distinguishes it from related tools such as get_deployment_logs or get_step_code by targeting step-run-scoped log records. The purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when the tool is appropriate and includes useful parameter usage constraints (logs_id cannot be combined with source). However, it does not explicitly contrast this tool with sibling tools like get_deployment_logs or get_step_code, leaving the selection decision partly to inference.

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

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