LinkedIn Profile Data Mining Server

Accepts natural language search queries and automatically expands them using LLM-based query generation to improve search coverage across LinkedIn's API. The system analyzes user intent, generates semantic variations and related keywords, and executes multiple parallel searches against LinkedIn's search endpoints, then deduplicates and ranks results by relevance. This enables finding profiles that wouldn't match literal keyword searches.

Aggregates professional profile data from multiple sources (LinkedIn, company websites, public databases) and cross-validates information to ensure accuracy and completeness. The system fetches data from each source, normalizes field mappings, detects conflicts, and applies confidence scoring based on source reliability and data freshness. Returns a unified profile object with enriched fields and validation metadata.

Applies multi-dimensional filtering to profile search results using structured criteria (location, industry, company size, seniority, skills, experience duration). The system supports both simple AND/OR logic and complex nested filters, enabling precise audience segmentation. Filters are applied server-side before returning results, reducing client-side processing and enabling efficient pagination of large result sets.

Extracts contact details (email, phone, social profiles) from LinkedIn profiles and enriches them using email verification APIs and secondary sources. The system parses profile data for explicit contact information, applies pattern matching to infer email addresses from company domain and name patterns, and validates extracted emails through SMTP verification or third-party email validation services. Returns verified contact information with confidence scores.

Exports profile search results and enriched data to CSV format with customizable column selection, formatting, and encoding. The system supports batch export of large result sets with streaming to avoid memory overload, applies data transformation rules (e.g., flattening nested objects, formatting dates), and handles special characters and encoding issues. Exported files can be imported directly into CRM, ATS, or spreadsheet applications.

Maintains a server-side cache of enriched profiles with automatic deduplication based on email, LinkedIn ID, and other unique identifiers. The system stores profiles in persistent storage (database or file system), implements cache invalidation strategies based on data freshness requirements, and detects duplicate profiles across multiple searches. Enables efficient reuse of enriched data and prevents redundant API calls for previously fetched profiles.

Exposes profile search, enrichment, and export capabilities as MCP tools with standardized schema-based function calling. The system defines tool schemas for each capability, handles parameter validation and type coercion, and returns results in a format compatible with Claude and other MCP-compatible agents. Enables autonomous agents to discover and invoke profile research capabilities without hardcoded integrations.

Accepts batch requests for profile research on multiple queries or profiles, executes searches asynchronously, and provides job status tracking and result polling. The system queues batch jobs, distributes work across worker processes, implements exponential backoff for API rate limiting, and stores results for later retrieval. Enables efficient processing of large-scale profile research without blocking the client.

Normalizes profile data from different sources (LinkedIn, company databases, public records) to a unified schema with consistent field names, types, and formats. The system applies transformation rules (e.g., standardizing title capitalization, parsing dates, normalizing location formats), handles missing or null values with sensible defaults, and provides schema versioning for backward compatibility. Enables consistent data handling across heterogeneous sources.