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Apollo.io MCP Server

enrich_organization

Enrich company data by domain to obtain detailed organization information including industry, employee count, and contact details.

Instructions

Enrich organization/company information.

This tool enriches company data based on domain, returning detailed information about the organization including industry, employee count, contact info, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'enrich_organization' tool, decorated with @mcp.tool() for registration. It parses the request, validates using OrganizationEnrichmentRequest schema, calls the Apollo.io /v1/organizations/enrich API endpoint, and returns the enriched organization data or error.
    @mcp.tool()
    async def enrich_organization(request: Union[Dict[str, Any], str]) -> Dict[str, Any]:
        """
        Enrich organization/company information.
        
        This tool enriches company data based on domain, returning detailed information
        about the organization including industry, employee count, contact info, and more.
        """
        endpoint = "/v1/organizations/enrich"
        
        # 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:
            org_request = OrganizationEnrichmentRequest(**request)
        except Exception as e:
            return {"error": f"Invalid request parameters: {str(e)}"}
        
        params = {"domain": org_request.domain}
        
        try:
            result = await apollo_client.make_request("GET", endpoint, params=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 for the enrich_organization tool, requiring a 'domain' field.
    class OrganizationEnrichmentRequest(BaseModel):
        """Request model for organization enrichment."""
        domain: str = Field(..., description="Company domain to enrich")
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 the tool returns detailed information (e.g., industry, employee count, contact info), which adds some context about output behavior. However, it lacks critical details such as data sources, rate limits, authentication requirements, or error handling, leaving significant gaps for a tool with no 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 appropriately concise with two sentences that efficiently convey the core functionality. The first sentence states the purpose, and the second elaborates on inputs and outputs without redundancy. However, it could be slightly more front-loaded by integrating key details earlier, but overall it's well-structured.

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 moderate complexity (enriching data), no annotations, low schema coverage (0%), but with an output schema present, the description is partially complete. It covers the basic purpose and output types but lacks details on parameters, behavioral traits, and usage context. The output schema mitigates some gaps, but overall it's adequate with clear room for improvement.

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 no enums, making it completely undocumented. The description adds minimal semantics by implying the parameter is a domain or related data, but it doesn't specify the exact format (e.g., string vs. object), required fields, or examples. This insufficiently compensates for the low schema coverage.

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: 'enriches company data based on domain, returning detailed information about the organization.' It specifies the verb ('enriches'), resource ('company data/organization'), and key input ('domain'). However, it doesn't explicitly differentiate from sibling tools like 'bulk_enrich_organizations' or 'enrich_person,' which would be needed for a perfect 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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'bulk_enrich_organizations' for multiple domains or 'enrich_person' for individual data, nor does it specify prerequisites or exclusions. The only implied context is enriching single organizations by domain, but this is minimal.

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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