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get_deployment_logs

Retrieve logs for a specific deployment to debug deployment issues or monitor behavior. Optionally filter by project and limit to recent log lines.

Instructions

Get logs for a specific deployment.

Retrieves logs from the deployment's underlying infrastructure. This is useful
for debugging deployment issues or monitoring deployment behavior.

Note: Log availability depends on the deployer plugin being installed and
the deployment infrastructure supporting log retrieval.

Args:
    name_id_or_prefix: The name, ID or prefix of the deployment
    project: Optional project scope (defaults to active project)
    tail: Number of recent log lines to retrieve (default: 100, max recommended: 500)

Returns:
    Dict with 'logs' (string) and metadata about truncation if applicable

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tail`	No
`project`	No
`name_id_or_prefix`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv2.0.0Sep 23, 2026

addedInput schema / additionalProperties
```
Added value: +false
```
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_deployment_logsOutput"New value: +"get_deployment_logsDictOutput"

First observedv1.2.0Jan 22, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses dependencies (deployer plugin, infrastructure support) and mentions return structure and truncation metadata, but does not explicitly state that the operation is read-only or mention any side effects. This is adequate but not exhaustive.

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

Conciseness4/5

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

The description is well-structured with a clear purpose, a dependency note, and a bulleted parameter list. Each sentence earns its place; it is not overly verbose and front-loads the core action.

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?

Given the tool's moderate complexity, the description covers key aspects: dependencies, return format, and parameter semantics. An output schema exists, so the explicit return summary is a bonus. It does not address error handling or performance characteristics, but these are not critical for a log retrieval tool.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate. It explains all three parameters in the Args section, including purpose, defaults, and a recommended max for 'tail'. This adds meaningful context beyond the bare schema, though it could go deeper on edge cases (e.g., behavior with non-existent deployment).

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

Purpose4/5

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

States a specific verb 'Get' with a clear resource 'logs for a specific deployment'. Differentiates from sibling get_step_logs implicitly via 'deployment' but does not name it explicitly, leaving a small gap in differentiation.

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

Usage Guidelines2/5

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

Provides general context ('useful for debugging deployment issues or monitoring deployment behavior') but offers no explicit guidance on when to use this tool versus get_step_logs or other siblings. No exclusions or alternative routing are mentioned, leaving the agent to infer the distinction.

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

Deploy Server