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Apollo.io MCP Server

organization_enrichment

Enrich company data by providing domain or name to access detailed organizational information through Apollo.io's database.

Instructions

Use the Organization Enrichment endpoint to enrich data for 1 company

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoCompany domain
nameNoCompany name

Implementation Reference

  • src/index.ts:112-128 (registration)
    Registration of the 'organization_enrichment' tool in the MCP server's list of tools, including input schema definition.
    {
      name: 'organization_enrichment',
      description: 'Use the Organization Enrichment endpoint to enrich data for 1 company',
      inputSchema: {
        type: 'object',
        properties: {
          domain: { 
            type: 'string', 
            description: 'Company domain' 
          },
          name: { 
            type: 'string', 
            description: 'Company name' 
          }
        }
      }
    },
  • MCP tool dispatch handler case for 'organization_enrichment' that invokes ApolloClient.organizationEnrichment and formats the response.
    case 'organization_enrichment': {
      const result = await this.apollo.organizationEnrichment(args);
      return {
        content: [{
          type: 'text',
          text: JSON.stringify(result, null, 2)
        }]
      };
    }
  • TypeScript interface defining the query parameters for organization enrichment (matches tool input schema).
    export interface OrganizationEnrichmentQuery {
      domain?: string;
      name?: string;
      [key: string]: any;
    }
  • Core handler implementation that executes the organization enrichment by calling the Apollo.io /organizations/enrich API endpoint.
    async organizationEnrichment(query: OrganizationEnrichmentQuery): Promise<any> {
      try {
        const url = `${this.baseUrl}/organizations/enrich`;
        const response = await this.axiosInstance.get(url, { params: query });
        
        if (response.status === 200) {
          return response.data;
        } else {
          console.error(`Error: ${response.status} - ${response.statusText}`);
          return null;
        }
      } catch (error: any) {
        console.error(`Error: ${error.response?.status} - ${error.response?.statusText || error.message}`);
        return null;
      }
    }
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 enriches data but doesn't explain what 'enrich' entails (e.g., adding details like industry, size, location), whether it's a read-only or mutative operation, or any constraints like rate limits or authentication needs. This lack of detail makes it inadequate for a tool with potential complexity.

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, efficient sentence that directly states the tool's function without any fluff. It's front-loaded with the core action ('enrich data') and resource ('1 company'), making it easy to parse. Every word earns its place, achieving optimal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete for a data enrichment tool. It doesn't explain what enriched data is returned (e.g., company details, metrics), how results are structured, or any behavioral traits like error handling. For a tool that likely provides rich output, this leaves significant gaps in understanding its full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with clear documentation for 'domain' and 'name'. The description adds no additional parameter semantics beyond implying it processes '1 company', which aligns with the schema but doesn't provide extra context like how parameters interact or which is prioritized. This meets the baseline for high 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: 'enrich data for 1 company' using the Organization Enrichment endpoint. It specifies the verb ('enrich') and resource ('data for 1 company'), making the intent unambiguous. However, it doesn't differentiate from sibling tools like 'organization_search' or 'people_enrichment', which prevents 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 when to prefer this over 'organization_search' for enrichment versus search, or over 'people_enrichment' for company versus individual data. There's no context about prerequisites, exclusions, or typical use cases, leaving the agent without operational direction.

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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