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by trustxai

airbyte_get_job_logs

Read-onlyIdempotent

Retrieve structured log entries for job sync attempts to debug failures. Limit output with attempt_number and 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 (internal API not available).

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
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds context: uses internal API (POST /v1/jobs/get_debug_info), returns JSON with structured log entries including metadata (timestamp, message, level, etc.), warns that logs can be large, and recommends using tail_lines/attempt_number to control output. This supplements annotations with actionable behavior details.

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?

Well-structured: starts with one-sentence purpose, then technical detail, usage guidelines, return format, and examples. No redundant sentences. Appropriate length given the tool's complexity—covers all necessary information without verbosity.

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?

Given the tool's complexity (internal API, structured logs, potential large output) and the presence of an output schema (description details the JSON structure with log entry fields), the description fully explains return format, limitations (Airbyte Cloud), and best practices (consuming as-is by LLMs). Covers all contexts an agent needs for correct invocation.

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% (context signal), but the input schema itself contains descriptions for each parameter (job_id, attempt_number, tail_lines). The description adds value by showing how parameters interact (e.g., using tail_lines to limit output, attempt_number to focus) and provides concrete examples illustrating typical usage patterns with default vs. adjusted values.

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 verb 'Get' and resource 'actual log output for a job's sync attempts'. It distinguishes from siblings like airbyte_get_job_details by specifying it returns raw logs, not structured failure info, and contrasts with airbyte_get_attempt_logs (a sibling) by focusing on job-level logs. Examples further clarify scope.

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?

Provides explicit 'When to Use' and 'When NOT to Use' sections. States to use for debugging sync failures, searching errors, or when airbyte_get_job_details shows failure but more context is needed. Explicitly advises against using for structured failure info and alerts that internal API is unavailable on Airbyte Cloud.

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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