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Mailchimp MCP

Mailchimp MCP

by damientilman
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

update_campaign_feedback

Edit an existing campaign feedback comment by updating its message text to refine collaboration notes.

Instructions

Update the text of an existing campaign feedback comment.

Use to edit a previously left collaboration note. Use list_campaign_feedback to discover feedback IDs.

Args: campaign_id: Campaign ID the feedback belongs to. Obtain from list_campaigns. feedback_id: Feedback comment ID to update. Obtain from list_campaign_feedback. message: New comment text, replacing the previous message.

Returns: JSON with feedback_id, message, is_complete, block_id, updated_at.

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": {
      "campaign_id": "<campaign_id>",
      "feedback_id": "<feedback_id>",
      "message": "<message>"
    },
    "name": "update_campaign_feedback"
  }
}

Input Schema

NameRequiredDescriptionDefault
accountNo
messageYes
campaign_idYes
feedback_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.7.1

TDQS

A4.2/5.0
Behavior3/5

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

Annotations are all false, so the description carries burden. It states 'replacing the previous message,' which adds context about mutation. However, it does not disclose additional behavioral traits beyond what annotations imply, such as idempotency or side effects.

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?

The description is brief and front-loaded with the purpose. It includes a short Args/Returns section with no fluff. Minor redundancy in repeating feedback_id explanation but overall efficient.

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 simplicity, the description is complete: it states the purpose, usage, parameter meanings, and return fields. The existence of an output schema covers return value documentation, so no gaps remain.

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?

With 0% schema description coverage, the description explains each required parameter's meaning and how to obtain values (e.g., campaign_id from list_campaigns, feedback_id from list_campaign_feedback, message as new text). It misses the optional 'account' parameter but covers the key parameters well.

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 'Update' and the resource 'campaign feedback comment,' distinguishing it from sibling tools like create_campaign_feedback, delete_campaign_feedback, and list_campaign_feedback.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use this tool (edit a previously left comment) and how to discover necessary IDs via list_campaign_feedback. It lacks explicit 'when not to use' but provides clear context.

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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