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list_interests

Retrieve individual interest options (checkboxes or radio items) from a specified Mailchimp interest category. Accepts list ID, category ID, and optional count parameter.

Instructions

List interests (individual options) within a category — the actual checkbox/radio items.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
list_idYes
category_idYes
countNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `list_interests` handler function. Tool decorated with @mcp.tool(), accepts list_id, category_id, and count params. Calls Mailchimp API GET /lists/{list_id}/interest-categories/{category_id}/interests and returns formatted interest data.
    @mcp.tool()
    async def list_interests(list_id: str, category_id: str, count: int = 50) -> str:
        """List interests (individual options) within a category — the actual checkbox/radio items."""
        mc = get_client()
        data = await mc.get(
            f"/lists/{list_id}/interest-categories/{category_id}/interests",
            params={"count": min(count, 100)},
        )
        interests = []
        for i in data.get("interests", []):
            interests.append({
                "id": i.get("id", ""),
                "name": i.get("name", ""),
                "subscriber_count": i.get("subscriber_count", 0),
                "display_order": i.get("display_order", 0),
            })
        return _fmt({"total_items": data.get("total_items", 0), "interests": interests})
  • The tool registration via @mcp.tool() decorator on the `list_interests` async function. The FastMCP instance `mcp` handles tool registration from the decorator.
    @mcp.tool()
  • Input schema for list_interests: requires list_id (str), category_id (str), and optional count (int, default 50). Returns formatted JSON string.
    async def list_interests(list_id: str, category_id: str, count: int = 50) -> str:
  • The `_fmt` helper function used by list_interests to format output as indented JSON.
    def _fmt(data: Any) -> str:
        """Format response data as indented JSON string."""
        return json.dumps(data, indent=2, default=str)
Behavior3/5

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

No annotations provided, so description carries full burden. It states the tool lists interests but does not disclose if it is read-only, pagination behavior, or any side effects. The read-only nature is implied by the 'list' verb, but not explicitly stated.

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 a single sentence of 10 words, front-loaded with key information. It is concise, but could briefly mention parameters without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool is a simple list operation and an output schema exists, the description is adequate but minimal. It does not explain the purpose of each parameter or what the output contains, leaving some gaps for the agent.

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

Parameters2/5

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

Schema description coverage is 0%, so description should compensate. It mentions 'within a category' hinting at category_id, but does not explain list_id or count. The description adds minimal meaning beyond what the schema already provides.

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 'List' and the resource 'interests (individual options) within a category'. It distinguishes from sibling tool 'list_interest_categories' by noting that these are the actual checkbox/radio items, not the categories themselves.

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 implies use when needing individual options within a category, but does not explicitly state when not to use or compare to alternatives like 'list_interest_categories'. However, the sibling context makes the distinction clear.

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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