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phxdev1

People Data Labs MCP Server

search_people

Search for individuals using SQL-like queries to match specific criteria, enabling precise identification and retrieval of relevant profiles from People Data Labs' extensive dataset.

Instructions

Search for people matching specific criteria

Input Schema

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

Implementation Reference

  • The handler function that implements the core logic for the 'search_people' tool. It validates input, constructs the API request to People Data Labs '/person/search' endpoint (with dataType='person'), and returns the 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:398-399 (registration)
    Switch case that registers and routes incoming 'search_people' tool calls to the handleSearch handler with 'person' dataType.
    case 'search_people':
      return await this.handleSearch('person', request.params.arguments);
  • Tool registration in ListTools response, including name, description, and input schema definition for 'search_people'.
    name: 'search_people',
    description: 'Search for people matching specific criteria',
    inputSchema: {
      type: 'object',
      properties: {
        query: {
          type: 'string',
          description: 'SQL-like query to search for people',
        },
        size: {
          type: 'number',
          description: 'Number of results to return (max 100)',
          minimum: 1,
          maximum: 100,
        },
      },
      required: ['query'],
    },
  • Type guard helper function used to validate input arguments for the search_people tool (and other search tools).
    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 the full burden of behavioral disclosure but only states the basic action. It doesn't cover aspects like whether this is a read-only operation, potential rate limits, authentication needs, or what the response format looks like (e.g., pagination, result structure). This leaves significant gaps for a search tool.

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 with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration, making it easy to parse quickly.

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 no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, result handling, and differentiation from siblings, failing to provide enough context for effective agent use despite the concise structure.

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 schema description coverage is 100%, so the input schema already documents both parameters ('query' as SQL-like search and 'size' with constraints). The description adds no additional meaning beyond this, such as examples of query syntax or usage tips, resulting in a baseline score of 3.

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 states the tool's purpose as searching for people with specific criteria, which is clear but vague. It uses a specific verb ('search') and resource ('people'), but doesn't distinguish from sibling tools like 'enrich_person' or 'search_companies', leaving ambiguity about scope and differentiation.

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 choose 'search_people' over sibling tools like 'enrich_person' or 'search_companies', nor does it specify prerequisites or exclusions, leaving usage context unclear.

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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