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airbyte_get_job_logs

Read-onlyIdempotent

Retrieve structured log output for a job's sync attempts to debug failures. Filter by attempt number and limit lines with tail_lines.

Instructions

Get the actual log output for a job's sync attempts.

Uses the internal Configuration API (POST /v1/jobs/get_debug_info) to fetch structured log entries for each attempt. Logs can be very large, so use tail_lines to limit output and attempt_number to focus on a specific attempt.

Always returns JSON — structured logs are best consumed as-is by LLMs and scripts. Each log entry contains timestamp, message, level, logSource, and caller metadata.

When to Use: - You need the raw log output to debug a sync failure. - You want to search for specific error messages or stack traces in the logs. - airbyte_get_job_details showed a failure but you need more context from the full logs.

When NOT to Use: - For structured failure info, use airbyte_get_job_details. - On Airbyte Cloud — use airbyte_get_cloud_sync_logs for full-text logs.

Returns: JSON with structured log entries per attempt, truncated to the last tail_lines entries.

Examples: Last 200 entries for all attempts: params = { "job_id": 12345 } Last 500 entries for attempt 0 only: params = { "job_id": 12345, "attempt_number": 0, "tail_lines": 500 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds that it always returns JSON with specific fields, logs can be large, and explains tail_lines/attempt_number behavior. No contradictions.

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?

Well-structured with sections, front-loaded purpose, and no redundancy. Slightly lengthy but every sentence adds value.

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

Completeness5/5

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

Covers purpose, usage, parameters, output format, examples, and alternatives. Output schema exists, so return value details are acceptable. Fully adequate for this 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?

Although schema descriptions exist, the description adds context via examples and explains default/limit behavior for tail_lines and attempt_number. This supplements the schema well.

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 clearly states the tool gets log output for a job's sync attempts. It specifies the verb 'get' and resource 'log output', and distinguishes itself from siblings like airbyte_get_job_details and airbyte_get_cloud_sync_logs.

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

Usage Guidelines5/5

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

Explicit 'When to Use' and 'When NOT to Use' sections provide clear guidance, including alternatives for structured failure info and Airbyte Cloud usage.

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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