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jgottlieb84

agentline-mcp

by jgottlieb84

create_email_address

Provision an email address to receive verification codes and emails from online services. Optionally set a custom local part; leaves auto-generated if omitted. Returns full address and status.

Instructions

Provision a new email address for sending and receiving mail. Use this when signing up for services that require email verification — the agent gets a real inbox.

local_part is the part before the @ (e.g. "my-agent"); leave null to auto-generate. Returns id, email_address (full address to paste into forms), provider, status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
local_partNo

Implementation Reference

  • The MCP tool handler for create_email_address — decorates with @mcp.tool(), accepts optional local_part, delegates to the Agentline SDK client, and returns the result as a dict.
    @mcp.tool()
    def create_email_address(local_part: str | None = None) -> dict:
        """Provision a new email address for sending and receiving mail. Use this
        when signing up for services that require email verification — the agent
        gets a real inbox.
    
        `local_part` is the part before the @ (e.g. "my-agent"); leave null to
        auto-generate. Returns `id`, `email_address` (full address to paste into
        forms), `provider`, `status`.
        """
        try:
            result = _client_or_init().create_email_address(local_part=local_part)
            return asdict(result)
        except AgentlineError as e:
            return {"error": str(e), "status_code": e.status_code}
  • Tool registration via FastMCP @mcp.tool() decorator on line 264, which registers the function as an MCP tool named 'create_email_address'.
    @mcp.tool()
    def create_email_address(local_part: str | None = None) -> dict:
  • Docstring describing the input schema (optional local_part) and output shape (id, email_address, provider, status).
    `local_part` is the part before the @ (e.g. "my-agent"); leave null to
    auto-generate. Returns `id`, `email_address` (full address to paste into
    forms), `provider`, `status`.
  • Helper function that lazily initializes and caches the Agentline SDK client used by the handler.
    def _client_or_init() -> Agentline:
        global _client
        if _client is None:
            _client = _build_client()
        return _client
Behavior4/5

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

No annotations are provided, so the description must carry the full burden. It discloses that the agent gets a real inbox and returns specific fields (id, email_address, provider, status). However, it does not mention rate limits, authentication requirements, or any potential 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus a parameter explanation and return value list. It is front-loaded with the primary action and usage context, then details. Every sentence earns its place with no wasted words.

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 low complexity (1 parameter, no output schema), the description is complete. It covers the tool's purpose, usage scenario, parameter behavior, and return values. No gaps or missing information.

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?

Schema coverage is 0%, so the description must compensate. It explains the purpose of local_part, states it can be null to auto-generate, and provides an example ('my-agent'). This adds significant meaning beyond the schema.

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 tool provisions a new email address for sending and receiving mail. It uses a specific verb (Provision) and resource (email address), and distinguishes from siblings like release_email_address, list_email_addresses, and send_email.

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?

Describes when to use the tool: 'when signing up for services that require email verification'. It does not explicitly state when not to use it, but the context is clear and the usage scenario is well-defined.

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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