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by trustxai

airbyte_get_cloud_sync_logs

Read-onlyIdempotent

Fetch full-text sync logs for an Airbyte Cloud connection job attempt. Supports paginated tail or head reads, with optional job and attempt parameters.

Instructions

Get full-text sync logs for an Airbyte Cloud connection job attempt.

Uses the Airbyte Cloud Config API (POST /jobs/get) to fetch embedded attempt logs and returns plain text with pagination metadata. This is the Cloud parity path — self-managed deployments should use airbyte_get_job_logs or airbyte_get_attempt_logs for richer structured diagnostics instead.

When to Use: - You are on Airbyte Cloud and need the raw log text for a sync. - You want paginated tail/head reads of a large Cloud log file. - You already know the connection and optionally the job/attempt.

When NOT to Use: - On self-managed Airbyte (abctl / OSS) — use the internal-API log tools. - When you need structured failure metadata — use airbyte_get_job_details on self-managed.

Returns: JSON with job_id, attempt_number, log_text, log_text_start_line, log_text_line_count, and total_log_lines_available.

Examples: Latest job, last 4000 lines: params = { "connection_id": "a1b2c3d4-..." } Specific job and attempt: params = { "connection_id": "a1b2c3d4-...", "job_id": 12345, "attempt_number": 0, "max_lines": 1000, }

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by noting it uses the Airbyte Cloud Config API (POST /jobs/get) and returns plain text with pagination metadata and a JSON object with specific fields. This provides additional transparency beyond annotations, though the safety profile is already well-covered.

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 yet comprehensive: a brief summary, technical note, usage sections, return field list, and examples. It is well-structured and front-loaded with the main purpose, making it easy for an AI agent to quickly grasp the tool's function.

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 (multiple optional parameters, pagination, Cloud vs self-managed), the description covers purpose, usage conditions, alternatives, return format, and examples. The output schema exists (as indicated), so return value explanation is sufficient. The description is complete for an AI agent to select and invoke the tool correctly.

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

Parameters3/5

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

The input schema has detailed descriptions for all parameters, so schema_description_coverage is high (despite the context signal saying 0%, the schema shows descriptions). The description adds examples of parameter usage, which is helpful but not essential. With high schema coverage, the baseline is 3, and the description does not significantly add beyond the schema.

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 'Get full-text sync logs for an Airbyte Cloud connection job attempt,' specifying the verb, resource, and scope. It distinguishes itself from sibling tools airbyte_get_job_logs and airbyte_get_attempt_logs by noting it is the 'Cloud parity path' for Airbyte Cloud, whereas the siblings are for self-managed deployments.

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?

The description includes explicit 'When to Use' and 'When NOT to Use' sections, providing clear context: use on Airbyte Cloud for raw log text with pagination, avoid on self-managed deployments or when structured failure metadata is needed (use airbyte_get_job_details instead). This gives strong usage guidance and alternatives.

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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