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Apollo.io MCP Server

by adamanz

organization_job_postings

Retrieve job postings for a specific organization using its Apollo.io ID to identify hiring needs and opportunities.

Instructions

Use the Organization Job Postings endpoint to find job postings for a specific organization

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
organization_idYesApollo.io organization ID

Implementation Reference

  • MCP CallToolRequestSchema handler case that executes the organization_job_postings tool by calling the Apollo client's organizationJobPostings method and formatting the response.
    case 'organization_job_postings': {
      const result = await this.apollo.organizationJobPostings(args.organization_id as string);
      return {
        content: [{
          type: 'text',
          text: JSON.stringify(result, null, 2)
        }]
      };
  • Core implementation of the organization job postings functionality, making the GET request to Apollo.io API endpoint /organizations/{organizationId}/job_postings.
    async organizationJobPostings(organizationId: string): Promise<any> {
      try {
        const url = `${this.baseUrl}/organizations/${organizationId}/job_postings`;
        const response = await this.axiosInstance.get(url);
        
        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;
      }
  • Input schema defining the required 'organization_id' parameter for the tool.
    inputSchema: {
      type: 'object',
      properties: {
        organization_id: { 
          type: 'string', 
          description: 'Apollo.io organization ID' 
        }
      },
      required: ['organization_id']
    }
  • src/index.ts:172-185 (registration)
    Tool registration object added to the tools list returned by ListToolsRequestSchema handler.
    {
      name: 'organization_job_postings',
      description: 'Use the Organization Job Postings endpoint to find job postings for a specific organization',
      inputSchema: {
        type: 'object',
        properties: {
          organization_id: { 
            type: 'string', 
            description: 'Apollo.io organization ID' 
          }
        },
        required: ['organization_id']
      }
    },
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 states the tool 'find[s] job postings' but doesn't describe how it behaves: e.g., whether it returns a list, supports pagination, requires authentication, has rate limits, or what happens if no job postings exist. For a read operation with zero annotation coverage, 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 appropriately sized (one sentence) and front-loaded with the core purpose. It avoids redundancy and wastes no words, making it efficient. However, it could be slightly more structured by separating purpose from context, but this is minor.

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 tool's complexity (a read operation with one parameter) and lack of annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of job postings, error handling), behavioral traits, or usage context. For a tool with no structured output information, the description should provide more completeness to guide the agent effectively.

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 description adds minimal meaning beyond the input schema. It implies the 'organization_id' parameter is used to specify the organization, but the schema already has 100% coverage with a clear description ('Apollo.io organization ID'). No additional details on parameter usage, constraints, or examples are provided. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.

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: 'find job postings for a specific organization' with a specific resource (job postings) and target (organization). It distinguishes from siblings like 'employees_of_company' or 'organization_search' by focusing on job postings rather than employees or general organization data. However, it doesn't explicitly contrast with all siblings (e.g., 'people_search' might also find job-related data).

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 (e.g., needing an organization ID), exclusions (e.g., not for personal job searches), or comparisons to sibling tools like 'organization_search' (which might return broader organization data). The phrase 'for a specific organization' implies a context but lacks explicit usage rules.

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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