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phxdev1

People Data Labs MCP Server

search_job_titles

Search for job titles using SQL-like queries to find specific roles. Retrieve relevant results up to a maximum of 100, enabling precise filtering and matching for job title data.

Instructions

Search for job titles matching specific criteria

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSQL-like query to search for job titles
sizeNoNumber of results to return (max 100)

Implementation Reference

  • Core handler function that performs the API call to People Data Labs for searching job titles (dataType='job_title'). Validates input, constructs query params, makes GET request to /job_title/search, and returns formatted JSON response.
    private async handleSearch(dataType: string, args: any) {
      if (!isValidSearchArgs(args)) {
        throw new McpError(
          ErrorCode.InvalidParams,
          `Invalid search parameters. Must provide a query string.`
        );
      }
    
      const params: Record<string, any> = {
        sql: args.query,
        size: args.size || 10,
      };
    
      const response = await pdlApi.get(`/${dataType}/search`, { params });
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(response.data, null, 2),
          },
        ],
      };
    }
  • src/index.ts:321-339 (registration)
    Registers the 'search_job_titles' tool with the MCP server in the ListTools response, including name, description, and input schema definition.
      name: 'search_job_titles',
      description: 'Search for job titles matching specific criteria',
      inputSchema: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'SQL-like query to search for job titles',
          },
          size: {
            type: 'number',
            description: 'Number of results to return (max 100)',
            minimum: 1,
            maximum: 100,
          },
        },
        required: ['query'],
      },
    },
  • Dispatch logic in the CallToolRequest handler that maps 'search_job_titles' tool invocations to handleSearch('job_title', arguments).
    case 'search_job_titles':
      return await this.handleSearch('job_title', request.params.arguments);
  • Input validation helper used by the search_job_titles handler to ensure query is provided and size is within bounds.
    const isValidSearchArgs = (args: any): args is {
      query: string;
      size?: number;
    } => {
      return typeof args === 'object' &&
             args !== null &&
             typeof args.query === 'string' &&
             (args.size === undefined || (typeof args.size === 'number' && args.size > 0 && args.size <= 100));
    };
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions 'matching specific criteria' but doesn't disclose key traits like whether this is a read-only operation, how results are returned (e.g., pagination, sorting), or any limitations (e.g., rate limits, authentication needs). The description is too vague to inform agent behavior effectively.

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 a single, efficient sentence with no wasted words. It's front-loaded with the core action ('Search for job titles'), making it easy to parse. However, it could be more structured by explicitly separating purpose from constraints, but its brevity is appropriate for the minimal content provided.

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 complexity of a search tool with 2 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, result format, and usage context. While the schema covers parameters well, the description fails to provide sufficient context for an agent to understand how to invoke and interpret this tool 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 no parameter semantics beyond what the input schema provides. Schema description coverage is 100%, with clear documentation for 'query' (SQL-like search) and 'size' (result count with max 100). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate or add extra meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Search for job titles matching specific criteria' clearly states the verb ('search') and resource ('job titles'), but it's vague about what 'specific criteria' entails. It distinguishes from siblings like 'search_people' or 'search_companies' by focusing on job titles, but lacks specificity about scope or functionality beyond basic search.

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 'search_skills' or 'autocomplete' for related tasks. Usage is implied by the name and description alone, with no explicit context for 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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