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damientilman

Mailchimp MCP

get_batch_status

Check progress and completion status of an asynchronous batch operation. Use after create_batch to poll until status is 'finished'.

Instructions

Check the progress and completion status of an asynchronous batch operation.

Use after create_batch to poll for completion. Call repeatedly until status is 'finished'. Do not use for non-batch operations. Use list_batches to see all recent batch operations.

Authenticated via API key. Subject to Mailchimp API rate limits (max 10 concurrent requests). Read-only, safe to retry.

Args: batch_id: The batch operation ID (e.g. 'batch123abc'). Obtain from create_batch.

Returns: JSON with fields: id, status ('pending' = queued, 'started' = in progress, 'finished' = complete), total_operations (int), finished_operations (int), errored_operations (int), submitted_at (ISO 8601), completed_at (ISO 8601 or null), response_body_url (string, downloadable tar.gz archive with per-operation results, only available when status is 'finished').

Example: get_batch_status(batch_id="batch123") -> {"status": "finished", "total_operations": 100, "finished_operations": 100, "errored_operations": 2, "response_body_url": "https://...", ...}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batch_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Discloses authentication (API key), rate limits (max 10 concurrent), read-only nature (safe to retry), and status values with response_body_url availability.

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 with sections (description, usage, params, returns, example). Every sentence adds value, no fluff.

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 input, output, usage pattern, polling behavior, error handling, and integration with other tools. Despite output schema existence, description adds narrative context.

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?

Explains batch_id parameter with example and source (obtain from create_batch), compensating for 0% schema coverage.

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?

Clearly states the tool checks progress and completion status of async batch operations. Explicitly distinguishes from siblings create_batch and list_batches.

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 (after create_batch for polling) and when-not (non-batch operations), with alternative (list_batches).

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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