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Apollo.io MCP Server

get_email_accounts

Retrieve configured email accounts from Apollo.io for sending sequences and campaigns. Lists available accounts for sales and marketing outreach.

Instructions

Retrieve email accounts associated with your Apollo.io account.

This tool returns the list of configured email accounts for sending sequences and campaigns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function decorated with @mcp.tool() that implements the get_email_accounts tool by making a GET request to the Apollo.io /v1/email_accounts endpoint using the shared ApolloAPIClient.
    @mcp.tool()
    async def get_email_accounts() -> Dict[str, Any]:
        """
        Retrieve email accounts associated with your Apollo.io account.
        
        This tool returns the list of configured email accounts for sending
        sequences and campaigns.
        """
        endpoint = "/v1/email_accounts"
        
        try:
            result = await apollo_client.make_request("GET", endpoint)
            return result
        except httpx.HTTPStatusError as e:
            return {"error": f"API request failed: {e.response.status_code} {e.response.text}"}
        except Exception as e:
            return {"error": f"Request failed: {str(e)}"}
  • The make_request method of ApolloAPIClient class, used by get_email_accounts to perform the API call.
    async def make_request(
        self, 
        method: str, 
        endpoint: str, 
        params: Optional[Dict] = None, 
        data: Optional[Dict] = None
    ) -> Dict[str, Any]:
        """Make an authenticated request to the Apollo.io API."""
        url = f"{self.base_url}{endpoint}"
        
        async with httpx.AsyncClient() as client:
            if method.upper() == "GET":
                response = await client.get(url, headers=self.headers, params=params)
            elif method.upper() == "POST":
                response = await client.post(url, headers=self.headers, json=data)
            elif method.upper() == "PUT":
                response = await client.put(url, headers=self.headers, json=data)
            else:
                raise ValueError(f"Unsupported HTTP method: {method}")
            
            response.raise_for_status()
            return response.json()
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'returns the list of configured email accounts' but doesn't cover critical aspects like authentication needs, rate limits, error handling, or whether it's a read-only operation. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 efficiently structured in two sentences: the first states the core purpose, and the second clarifies the return value. There's no wasted language, and it's front-loaded with the main action, making it easy for an agent to parse quickly.

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's low complexity (0 parameters) and the presence of an output schema, the description is minimally adequate. However, with no annotations and sibling tools present, it lacks context on usage scenarios and behavioral traits, which could hinder the agent's ability to invoke it correctly in practice.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose. A baseline of 4 is applied since no parameters exist and schema coverage is complete.

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 tool's purpose with 'Retrieve email accounts associated with your Apollo.io account' and specifies the resource (email accounts) and context (Apollo.io). However, it doesn't explicitly differentiate from sibling tools like 'get_account_by_id' or 'search_accounts' that might also retrieve account information, missing full sibling distinction.

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. It doesn't mention prerequisites, exclusions, or compare to sibling tools like 'get_account_by_id' or 'search_accounts', leaving the agent without context for tool selection.

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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