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create_segment

Create static segments in Mailchimp by providing a list of email addresses to organize subscribers for targeted email campaigns.

Instructions

Create a static segment from email addresses. emails: comma-separated list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
list_idYes
nameYes
emailsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Implementation of the create_segment tool handler, which creates a static segment in Mailchimp.
    @mcp.tool()
    async def create_segment(
        list_id: str,
        name: str,
        emails: str = "",
    ) -> str:
        """Create a static segment from email addresses. emails: comma-separated list."""
        mc = get_client()
        body: dict[str, Any] = {"name": name}
        if emails:
            body["static_segment"] = [e.strip() for e in emails.split(",") if e.strip()]
        s = await mc.post(f"/lists/{list_id}/segments", json=body)
        return _fmt({
            "id": s.get("id", ""),
            "name": s.get("name", ""),
            "member_count": s.get("member_count", 0),
            "message": "Segment created.",
        })
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Create' which implies a write operation, but fails to detail permissions, side effects, error handling, or response behavior. This is inadequate for a mutation tool with zero annotation coverage.

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 to the point, consisting of two concise sentences. It avoids unnecessary verbosity, though it could be more structured by front-loading critical information about all parameters.

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 that there is an output schema, the description does not need to explain return values. However, as a mutation tool with no annotations and low parameter coverage, it should provide more context on behavior and usage. The description is minimally adequate but has clear gaps.

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 the description must compensate. It only mentions the 'emails' parameter as a 'comma-separated list', ignoring 'list_id' and 'name' which are required. This adds minimal value beyond the schema, leaving key parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a static segment') and the resource ('from email addresses'), specifying the verb and resource. However, it does not differentiate from sibling tools like 'create_audience' or 'list_segments', which could create ambiguity in tool selection.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as 'create_audience' or 'list_segments'. It lacks context about prerequisites, use cases, or exclusions, leaving the agent to infer usage from the tool name alone.

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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