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jgottlieb84

agentline-mcp

by jgottlieb84

list_email_addresses

Get a list of every email address provisioned on your account.

Instructions

List all email addresses currently provisioned on this account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The `list_email_addresses` function is the MCP tool handler. It is decorated with @mcp.tool(), calls the Agentline SDK client's `list_email_addresses()` method, and returns a dict with the list of addresses as dataclass-asdict results, wrapped in an 'addresses' key. Errors are caught and returned as dicts with 'error' and 'status_code'.
    @mcp.tool()
    def list_email_addresses() -> dict:
        """List all email addresses currently provisioned on this account."""
        try:
            addrs = _client_or_init().list_email_addresses()
            return {"addresses": [asdict(a) for a in addrs]}
        except AgentlineError as e:
            return {"error": str(e), "status_code": e.status_code}
  • The tool is registered with MCP via the @mcp.tool() decorator on line 281, where `mcp` is a FastMCP instance created on line 47 of the same file.
    @mcp.tool()
    def list_email_addresses() -> dict:
Behavior2/5

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

With no annotations provided, the description carries full burden. It states 'list' which implies a read operation, but lacks details on authentication, rate limits, or return format. No side effects are disclosed, leaving the agent underinformed about behavioral implications beyond the rudimentary action.

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?

The description is a single, front-loaded sentence with zero wasted words. It immediately conveys the tool's purpose without unnecessary elaboration, earning a perfect score for conciseness.

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 no output schema and no annotations, the description is minimally adequate: it tells what the tool does but not the format of the returned data or any constraints. For a simple list operation, it is mostly complete, but could benefit from specifying the type of output (e.g., list of strings).

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?

The input schema has zero parameters, and the description adds no parameter-level detail since there are none. Per rubric, 0 parameters yields a baseline of 4, and the description does not miss any parameter documentation.

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 'List all email addresses currently provisioned on this account.' It specifies the verb (list), resource (email addresses), and scope (all, provisioned, on this account), distinguishing it from sibling tools like create_email_address and release_email_address.

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?

No explicit guidance on when to use this tool versus alternatives. The description does not mention when to use list_numbers or other list tools, nor does it exclude scenarios. The context of siblings suggests it's for read operations, but no when-to-use or when-not-to-use information is provided.

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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