Gemini MCP Server

batch_get_status

Check batch job status and optionally wait for completion with auto-polling. Returns current state, progress statistics, and completion details for large-scale AI processing tasks.

Instructions

GET BATCH JOB STATUS - Check status of running batch job with optional auto-polling. STATES: PENDING (queued), RUNNING (processing), SUCCEEDED (complete), FAILED (error), CANCELLED (user stopped), EXPIRED (timeout). WORKFLOW: 1) Call with batch job name/ID, 2) Optionally enable polling to wait for completion, 3) Returns current state, progress stats, and completion info. USAGE: Pass job name from batch_create response. Enable autoPoll for hands-off waiting.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "batchName": "<batchName>"
    },
    "name": "batch_get_status"
  }
}

Input Schema

Name	Required	Description
`batchName`	Yes	Batch job name/ID from batch_create
`autoPoll`	No	Automatically poll until job completes (SUCCEEDED, FAILED, or CANCELLED)
`pollIntervalSeconds`	No	Seconds between status checks when autoPoll=true (default: 30)
`maxWaitMs`	No	Maximum wait time in milliseconds (default: 24 hours)

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 26, 2025

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by detailing the job states (PENDING, RUNNING, etc.), the workflow steps, and the polling behavior. It explains what the tool returns (current state, progress stats, completion info) and the auto-polling mechanism. Minor gap: doesn't mention error handling or rate limits.

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 clear sections (STATES, WORKFLOW, USAGE) and front-loaded purpose. Slightly verbose with some redundancy (e.g., 'optional auto-polling' then 'Optionally enable polling'), but every sentence adds value. Could be more streamlined.

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?

For a tool with no annotations and no output schema, the description provides substantial context: states, workflow, usage guidance, and behavioral details. It adequately compensates for the lack of structured fields, though it could benefit from explicitly mentioning the return format or error cases.

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?

Schema description coverage is 100%, so the schema already fully documents all parameters. The description adds some context by mentioning 'batch job name/ID' and 'auto-polling', but doesn't provide additional semantic meaning beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('GET BATCH JOB STATUS - Check status of running batch job') and distinguishes it from siblings like batch_cancel, batch_create, etc. It specifies the resource (batch job) and verb (check status) with precision.

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 guidance is provided: 'USAGE: Pass job name from batch_create response. Enable autoPoll for hands-off waiting.' This tells the agent exactly when to use this tool (after batch_create) and how to configure it for different scenarios (hands-off vs manual checking).

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