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gam_jobs

Check and manage the status of asynchronous Google Ad Manager background jobs. Use polling to monitor progress, retrieve completed results, list recent jobs, or cancel running operations.

Instructions

Poll and manage async GAM background jobs.

MODE: read-only (polling only — jobs are created by other tools) AUTH: OAuth 2.0 required CREDITS: 0 (free) OUTPUT: Returns Job objects with id, type, status (PENDING | RUNNING | COMPLETE | FAILED), progress (0–100), result_url or result_data, and error if failed. WHEN TO USE: Use after starting a long-running operation (e.g., large inventory scan, bulk line item creation) that returns a job_id. Poll until status = COMPLETE or FAILED.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesSub-operation to perform: • poll: Poll a job's current status and progress. Returns status and progress percentage. Recommended polling interval: 5 seconds. • get: Get a completed job's full result. Returns result_data or result_url for download. • list: List recent jobs for the current tenant. Read-only. • cancel: Cancel a running job. Write — interrupts the background operation.
network_codeNoGAM network code (e.g. 12345678). Required for all network-scoped operations. Obtain via select_gam_network or list_accessible_networks.
job_idNoAsync job identifier returned by a previous long-running operation.
Behavior5/5

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

With no annotations, the description fully discloses behavior: read-only mode, OAuth 2.0 requirement, free credits, output structure (Job objects with id, type, status, progress, result_url, error), and recommended poll interval of 5 seconds.

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 well-structured with labeled sections (MODE, AUTH, CREDITS, OUTPUT, WHEN TO USE), is front-loaded with the purpose, and every sentence adds value. Length is appropriate for the tool's complexity.

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 no output schema or annotations, the description is thorough: it covers all sub-operations, authentication, output format, usage context, and even credits. No critical information is missing for an agent to use the tool correctly.

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 100% with parameter descriptions, but the description adds meaningful context beyond the schema, such as the recommended polling interval for 'poll' and how to obtain network_code. This enhances usability without being redundant.

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 identifies the tool as managing async GAM background jobs, specifying 'Poll and manage async GAM background jobs.' It differentiates from the sibling 'jobs' tool by noting that jobs are created by other tools and this is read-only polling.

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 an explicit 'WHEN TO USE' section advising to use after starting a long-running operation and poll until completion. It also details the four sub-actions (poll, get, list, cancel) with their contexts.

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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