{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_amankale376-profile-researcher","slug":"amankale376-profile-researcher","name":"LinkedIn Profile Data Mining Server","type":"mcp","url":"https://smithery.ai/servers/amankale376/profile-researcher","page_url":"https://unfragile.ai/amankale376-profile-researcher","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:amankale376/profile-researcher"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_amankale376-profile-researcher__cap_0","uri":"capability://search.retrieval.ai.powered.linkedin.profile.search.with.query.expansion","name":"ai-powered linkedin profile search with query expansion","description":"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.","intents":["I want to find LinkedIn profiles matching a vague job description without manually crafting multiple search queries","I need to discover professionals with specific skill combinations that aren't explicitly listed in their profiles","I want to expand my search beyond exact keyword matches to catch related roles and titles"],"best_for":["Recruitment teams building talent pipelines","Sales development representatives prospecting at scale","AI agents that need to autonomously discover professional contacts"],"limitations":["Query expansion quality depends on LLM model capability; poor prompts yield irrelevant expansions","LinkedIn API rate limits constrain parallel search execution; high-volume queries may queue or fail","Semantic drift in expanded queries can return false positives requiring manual filtering","No guarantee of finding all matching profiles; LinkedIn's search algorithm is opaque"],"requires":["LinkedIn API credentials with search permissions","LLM API access (OpenAI, Anthropic, or compatible provider)","MCP client supporting tool calling (Claude, custom agents)","Network connectivity to LinkedIn and LLM provider"],"input_types":["text (natural language search query)","structured filters (location, industry, seniority level)"],"output_types":["structured JSON array of profile objects","profile metadata (name, title, company, location, URL)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_1","uri":"capability://data.processing.analysis.multi.source.profile.data.enrichment.and.validation","name":"multi-source profile data enrichment and validation","description":"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.","intents":["I need complete professional profiles with verified contact information from multiple sources","I want to identify outdated or conflicting information across different profile sources","I need to enrich LinkedIn profiles with additional data like company size, funding, or industry classification"],"best_for":["B2B sales and marketing teams requiring high-quality prospect data","HR teams conducting background research on candidates","Data enrichment pipelines that feed CRM or ATS systems"],"limitations":["Data freshness varies by source; some sources update infrequently causing stale information","Cross-source conflicts require heuristic resolution; no single source of truth","API availability of secondary sources is not guaranteed; enrichment may be partial if sources are down","Privacy regulations (GDPR, CCPA) may restrict data aggregation across sources in certain jurisdictions"],"requires":["LinkedIn API credentials","API keys for secondary data sources (company databases, public records APIs)","MCP server with persistent storage for caching enriched profiles","Normalization schema mapping fields across different source formats"],"input_types":["structured profile object (LinkedIn profile ID or email)","optional enrichment preferences (which sources to prioritize)"],"output_types":["unified profile JSON with merged fields","confidence scores per field","source attribution metadata","conflict resolution log"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_2","uri":"capability://data.processing.analysis.smart.filtering.and.segmentation.of.profile.results","name":"smart filtering and segmentation of profile results","description":"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.","intents":["I want to narrow search results to only profiles matching specific geographic, industry, or seniority criteria","I need to segment a large list of profiles into cohorts for targeted outreach campaigns","I want to exclude profiles that don't meet minimum experience or skill requirements"],"best_for":["Sales teams building targeted prospect lists","Recruitment teams filtering candidates by specific criteria","Marketing teams segmenting professional audiences for campaigns"],"limitations":["Filter accuracy depends on data quality; missing or incomplete profile fields reduce filtering precision","Complex nested filters may have performance impact on large datasets; query optimization required","Some filter criteria (e.g., 'actively looking for a job') are not available via LinkedIn API and must be inferred","Filter logic is server-side only; no client-side filtering capability for dynamic refinement"],"requires":["Structured filter schema definition","Profile data with populated fields matching filter criteria","MCP server with query optimization for complex filters"],"input_types":["structured filter object with AND/OR operators","filter criteria (location, industry, company, seniority, skills, experience)"],"output_types":["filtered profile array","filter match statistics (count per criterion)","pagination metadata"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_3","uri":"capability://data.processing.analysis.contact.information.extraction.and.enrichment","name":"contact information extraction and enrichment","description":"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.","intents":["I need to extract email addresses from LinkedIn profiles for outreach campaigns","I want to verify that extracted contact information is current and deliverable","I need to find alternative contact methods (phone, social profiles) when email is unavailable"],"best_for":["Sales development teams building prospect contact lists","Recruitment teams reaching out to candidates","Marketing teams conducting email outreach campaigns"],"limitations":["Email inference from name/company patterns has false positive rate; verification is essential","SMTP verification may trigger spam filters or rate limiting on target mail servers","LinkedIn API does not expose email addresses directly; extraction relies on secondary sources or inference","Contact information freshness is not guaranteed; people change jobs and email addresses frequently","Email validation APIs have rate limits and may incur additional costs"],"requires":["LinkedIn profile data with name and company information","Email validation API credentials (e.g., Hunter.io, RocketReach, Clearbit)","Optional: SMTP server access for direct email verification","Inference rules for email pattern matching (configurable per company domain)"],"input_types":["LinkedIn profile object","optional email validation preferences (verification method, confidence threshold)"],"output_types":["contact information object (email, phone, social profiles)","confidence score per contact method","verification status and timestamp"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_4","uri":"capability://data.processing.analysis.csv.export.and.bulk.data.management","name":"csv export and bulk data management","description":"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.","intents":["I want to export search results to CSV for use in my CRM or email outreach tool","I need to bulk download enriched profile data for analysis in Excel or Google Sheets","I want to customize which fields are included in the export to match my workflow"],"best_for":["Sales teams exporting prospect lists to CRM systems","Recruitment teams bulk downloading candidate profiles","Marketing teams preparing data for campaign tools"],"limitations":["Large exports (>100k rows) may be memory-intensive; streaming implementation required","CSV format has limitations for nested data; complex objects must be flattened or serialized","Character encoding issues may occur with international names or special characters","No built-in deduplication; duplicate profiles in results will appear in export","CSV files lack data validation; imported data may require cleaning in destination system"],"requires":["Profile data in structured JSON format","CSV export configuration (column selection, formatting rules)","Sufficient disk space for exported file"],"input_types":["profile array (JSON)","export configuration (column names, formatting options)"],"output_types":["CSV file","optional: compressed archive for large exports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_5","uri":"capability://memory.knowledge.persistent.profile.caching.and.deduplication","name":"persistent profile caching and deduplication","description":"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.","intents":["I want to avoid re-enriching profiles I've already researched in previous searches","I need to detect when the same person appears in multiple search results under different contexts","I want to maintain a persistent database of researched profiles for future reference"],"best_for":["Teams conducting ongoing recruitment or sales prospecting","Organizations building internal talent or prospect databases","AI agents that perform repeated profile research across multiple sessions"],"limitations":["Persistent storage requires external database or file system; adds operational complexity","Cache invalidation strategy must balance freshness vs API cost; stale data may be returned","Deduplication heuristics may have false negatives (missing duplicates) or false positives (incorrectly merging different people)","Storage costs scale with number of cached profiles; large caches may incur significant infrastructure costs","Privacy regulations may require data deletion; cache management must support GDPR/CCPA compliance"],"requires":["Persistent storage backend (PostgreSQL, MongoDB, SQLite, or file system)","Deduplication schema and matching rules","Cache invalidation policy (TTL, manual refresh, event-based)","MCP server with storage integration"],"input_types":["enriched profile object","cache configuration (TTL, deduplication rules)"],"output_types":["cached profile with metadata (cache timestamp, source, freshness)","deduplication report (merged profiles, confidence)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_6","uri":"capability://tool.use.integration.mcp.tool.calling.interface.for.agent.integration","name":"mcp tool calling interface for agent integration","description":"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.","intents":["I want to integrate profile research into an autonomous AI agent without custom code","I need my agent to discover available profile research capabilities dynamically","I want to chain profile research with other tools (email sending, CRM updates) in agent workflows"],"best_for":["AI agent developers building multi-tool workflows","Teams deploying autonomous sales or recruitment agents","Organizations integrating profile research into larger AI systems"],"limitations":["MCP protocol overhead adds latency (~100-200ms per tool call) compared to direct API calls","Tool schema must be manually defined and kept in sync with implementation; schema drift causes errors","Agent decision-making quality depends on tool descriptions; poor descriptions lead to misuse","No built-in error recovery; agents must handle tool failures explicitly","Rate limiting and quota management must be implemented at agent level, not tool level"],"requires":["MCP client implementation (Claude, custom agent framework)","Tool schema definitions for each capability","MCP server running and accessible to agent","Agent framework supporting tool calling (Claude API, LangChain, custom implementation)"],"input_types":["MCP tool call with parameters (JSON)","tool schema (JSON schema format)"],"output_types":["MCP tool result (JSON)","error response with error code and message"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_7","uri":"capability://automation.workflow.batch.profile.research.with.async.job.management","name":"batch profile research with async job management","description":"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.","intents":["I want to research hundreds of profiles without waiting for each one to complete sequentially","I need to schedule profile research jobs to run during off-peak hours to avoid rate limiting","I want to monitor the progress of large-scale profile research operations"],"best_for":["Teams conducting large-scale recruitment or sales prospecting campaigns","Organizations building automated profile research pipelines","AI agents performing bulk data collection tasks"],"limitations":["Async processing adds complexity; clients must implement polling or webhook handling for results","Job queue may have maximum size limits; very large batches may be rejected","Rate limiting may cause job delays; completion time is not predictable","Results storage is temporary; clients must retrieve results before expiration","No built-in retry logic for failed jobs; manual intervention may be required"],"requires":["Job queue system (Redis, RabbitMQ, or in-memory queue)","Worker process pool for parallel job execution","Result storage (database or cache) with TTL","Job status tracking and polling endpoint"],"input_types":["batch request array (multiple search queries or profile IDs)","batch configuration (priority, timeout, result retention)"],"output_types":["job ID for tracking","job status (queued, running, completed, failed)","batch results array (profiles with enrichment data)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_amankale376-profile-researcher__cap_8","uri":"capability://data.processing.analysis.profile.data.normalization.and.schema.mapping","name":"profile data normalization and schema mapping","description":"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.","intents":["I want to work with profile data from multiple sources using a consistent schema","I need to handle missing or inconsistent data from different sources gracefully","I want to ensure exported data matches my CRM or ATS schema requirements"],"best_for":["Teams integrating profile data from multiple sources","Organizations with strict data quality requirements","Data engineering teams building ETL pipelines"],"limitations":["Schema mapping rules must be manually defined and maintained; schema drift requires updates","Normalization may lose source-specific data; trade-off between consistency and fidelity","Handling of edge cases (unusual titles, international formats) requires custom rules","Schema versioning adds complexity; clients must handle multiple schema versions"],"requires":["Unified schema definition (JSON schema or similar)","Transformation rules for each source format","Schema versioning strategy"],"input_types":["raw profile data from source (JSON)","source type identifier"],"output_types":["normalized profile object matching unified schema","transformation metadata (applied rules, warnings)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["LinkedIn API credentials with search permissions","LLM API access (OpenAI, Anthropic, or compatible provider)","MCP client supporting tool calling (Claude, custom agents)","Network connectivity to LinkedIn and LLM provider","LinkedIn API credentials","API keys for secondary data sources (company databases, public records APIs)","MCP server with persistent storage for caching enriched profiles","Normalization schema mapping fields across different source formats","Structured filter schema definition","Profile data with populated fields matching filter criteria"],"failure_modes":["Query expansion quality depends on LLM model capability; poor prompts yield irrelevant expansions","LinkedIn API rate limits constrain parallel search execution; high-volume queries may queue or fail","Semantic drift in expanded queries can return false positives requiring manual filtering","No guarantee of finding all matching profiles; LinkedIn's search algorithm is opaque","Data freshness varies by source; some sources update infrequently causing stale information","Cross-source conflicts require heuristic resolution; no single source of truth","API availability of secondary sources is not guaranteed; enrichment may be partial if sources are down","Privacy regulations (GDPR, CCPA) may restrict data aggregation across sources in certain jurisdictions","Filter accuracy depends on data quality; missing or incomplete profile fields reduce filtering precision","Complex nested filters may have performance impact on large datasets; query optimization required","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.53,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:36.245Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=amankale376-profile-researcher","compare_url":"https://unfragile.ai/compare?artifact=amankale376-profile-researcher"}},"signature":"M1cr7wXxIOPb320jjBBVf97IVBxMO5GnfKIbrjEWISOr1qW9rYWQL85WsEGyqJhWD6dKUfo6SybP+8XIykrCCA==","signedAt":"2026-06-21T03:13:22.986Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/amankale376-profile-researcher","artifact":"https://unfragile.ai/amankale376-profile-researcher","verify":"https://unfragile.ai/api/v1/verify?slug=amankale376-profile-researcher","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}