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Apollo.io MCP Server

enrich_person

Enhance person profiles by retrieving contact details like email addresses and phone numbers from Apollo.io's database to support sales and marketing activities.

Instructions

Enrich a person's information and reveal contact details.

This tool enriches person data and can reveal email addresses and phone numbers. The more information provided, the higher the match likelihood.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function for the 'enrich_person' tool, decorated with @mcp.tool() for automatic MCP registration. It parses and validates input using PersonEnrichmentRequest Pydantic model, constructs API parameters, calls the Apollo.io /v1/people/match endpoint, and handles responses or errors.
    @mcp.tool()
    async def enrich_person(request: Union[Dict[str, Any], str]) -> Dict[str, Any]:
        """
        Enrich a person's information and reveal contact details.
        
        This tool enriches person data and can reveal email addresses and phone numbers.
        The more information provided, the higher the match likelihood.
        """
        endpoint = "/v1/people/match"
        
        # Handle both JSON string and dict inputs
        if isinstance(request, str):
            try:
                request = json.loads(request)
            except json.JSONDecodeError as e:
                return {"error": f"Invalid JSON in request: {str(e)}"}
        
        # Create and validate request object from dictionary
        try:
            person_request = PersonEnrichmentRequest(**request)
        except Exception as e:
            return {"error": f"Invalid request parameters: {str(e)}"}
        
        # Convert request to dict and remove None values
        enrich_params = {k: v for k, v in person_request.dict().items() if v is not None}
        
        try:
            result = await apollo_client.make_request("POST", endpoint, data=enrich_params)
            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)}"}
  • Pydantic model defining the input schema and validation for the enrich_person tool, specifying optional fields like names, email, organization details, LinkedIn URL, and flags for revealing contacts.
    class PersonEnrichmentRequest(BaseModel):
        """Request model for person enrichment."""
        first_name: Optional[str] = Field(None, description="Person's first name")
        last_name: Optional[str] = Field(None, description="Person's last name")
        email: Optional[str] = Field(None, description="Person's email address")
        organization_name: Optional[str] = Field(None, description="Company name")
        domain: Optional[str] = Field(None, description="Company domain")
        linkedin_url: Optional[str] = Field(None, description="LinkedIn profile URL")
        reveal_personal_emails: bool = Field(False, description="Whether to reveal personal emails")
        reveal_phone_number: bool = Field(False, description="Whether to reveal phone numbers")
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 that the tool 'can reveal email addresses and phone numbers', which hints at data retrieval, but it doesn't clarify if this is a read-only operation, requires authentication, has rate limits, or what happens on failure. For a tool with no annotations and potential privacy implications, this is a significant gap in transparency.

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 concise with three sentences that are front-loaded: the first sentence states the core purpose, the second elaborates slightly, and the third adds a usage tip. There's no wasted text, but the structure could be improved by more clearly separating purpose from guidelines.

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 complexity (enriching person data with contact details), no annotations, and an output schema exists (which should cover return values), the description is moderately complete. It states what the tool does but lacks details on behavioral traits and parameter usage. The presence of an output schema helps, but the description doesn't fully compensate for the missing annotations and low parameter coverage.

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?

The input schema has 1 parameter ('request') with 0% description coverage, and the description doesn't explain what 'request' should contain (e.g., person's name, ID, or other identifiers). It vaguely states 'The more information provided, the higher the match likelihood', but this doesn't add meaningful semantics beyond the schema. With low schema coverage, the description fails to compensate adequately.

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: 'Enrich a person's information and reveal contact details.' It specifies the verb ('enrich'), resource ('person's information'), and outcome ('reveal contact details'). However, it doesn't explicitly differentiate from sibling tools like 'search_people' or 'get_account_by_id', which might also retrieve person information, so it doesn't reach the highest score.

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 minimal guidance: 'The more information provided, the higher the match likelihood' implies that better input leads to better results, but it doesn't specify when to use this tool versus alternatives like 'search_people' or 'get_account_by_id'. There's no explicit context, exclusions, or named alternatives, leaving the agent with little direction on 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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