"Finding Ohad Zafrir's LinkedIn Profile" matching MCP servers:

An MCP server that provides access to the LinkedIn Bulk Data Scraper API and various utility tools for data extraction and processing.

Enables AI agents and terminal users to query LinkedIn Ads (Marketing API) data locally with their own token, covering ad accounts, campaigns, campaign groups, performance metrics, demographic breakdowns, creatives, lead form counts, and campaign targeting. All operations are read-only GET requests, so nothing can be created, edited, paused, or deleted, and lead response details are never fetched.

Analyse your LinkedIn Ads performance. Compare to benchmarks and get optimisation recommendations.

Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.

Enables finding and verifying work emails for contacts through a waterfall of provider keys, with per-provider cost attribution and verification after each finder.

GTM signal intelligence suite for AI agents. Six tools: hiring signals, tech stack detection, company-to-LinkedIn resolution, ICP scoring, job board scanning, and a combined signals aggregator. Built for outbound sales workflows.

Runs all GTM signal tools in a single call and returns a unified company intelligence report combining hiring, tech stack, LinkedIn, and job board data.

Enables validating CSV structure, checking simple schemas, converting between CSV and JSON, sampling rows, and finding duplicate keys on local files. All processing stays local, so no user data is ever uploaded.

Enables AI assistants and editors to profile, clean, and analyze messy CSV/Excel files directly through MCP tools such as profile_dataset, clean_dataset, and list_sheets.

An MCP server optimized for user profile and audience data synchronization tasks, providing tools for confirmation, verification, and rollback in data sync workflows.

Enables AI assistants to read, analyze, edit, and create Alteryx workflows, including summarizing workflows, finding tools, editing configurations, updating SQL, managing connections, and building new workflows programmatically.

Exposes local data profiling, PII classification, and plan-based cleansing against DuckDB, Postgres, or SQLMesh tables through four tools (list_tables, profile, propose_cleansing_plan, apply_cleansing_plan). All reasoning stays aggregate-only — format signatures, counts, and match rates rather than raw cell values — and cleaning plans execute only after validation against a fixed operation vocabulary and explicit confirmation.

This MCP server enables AI-assisted professional profile generation by connecting to verified employee work data (skills, career history, certifications, projects) from employer HCM systems.

Enables MCP-compatible clients to execute Databricks CLI commands without shell interpolation, with pipes and redirects stripped so clients can filter or format raw output. It supports any subcommand permitted by the configured Databricks authentication profile.

Enables AI agents to ingest CSV/TSV files, profile and categorize tables, generate SQL DDL for multiple engines, and launch background data workflows whose completion and artifacts can be independently verified.

Enables B2B prospecting directly inside Claude by sourcing companies from live job postings, enriching them with LinkedIn and Crunchbase firmographic and funding data, and scoring each account 0-100 with an auditable reason for every point. It also surfaces named decision-makers with verified work emails and stores, budgets, and exports the resulting leads locally without ever sending anything.

Syncs Google Search Console performance data into a local ClickHouse database and exposes prepared SEO analysis tools to AI agents. Enables diagnosing traffic drops, finding striking-distance keywords, detecting cannibalization, auditing indexing, and generating weekly SEO reports through natural language.

Enables AI assistants to validate CSV exports against schemas, infer schemas from example files, profile datasets, and diff before/after exports for lab/LIMS data-quality checks.

MCP server that turns operator-authorized local or synthetic source records into auditable research briefs, with every finding linked to exact excerpts and hashes. Exposes a single build_research_brief tool over deterministic, no-network stdio.

An MCP server that gives AI assistants the ability to connect to, query, profile, and monitor data sources — turning any LLM into an interactive data engineering copilot.

Enables AI assistants to audit, profile, and govern dbt projects through natural language, including coverage scoring, schema drift detection, test suggestions, and non-destructive YAML documentation updates.

Enables AI assistants to audit dbt project health, profile data, detect schema drift, suggest tests, and auto-generate documentation through natural language. It consolidates coverage analysis, data profiling, and safe YAML updates into a single MCP interface.

Enables AI assistants to audit dbt project coverage and health, profile warehouse data, detect schema drift, suggest tests, and generate or safely update model documentation through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and auto-generate documentation and test suggestions through natural language. It provides tooling for coverage scoring, batched column statistics, and non-destructive schema.yml updates.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and auto-generate documentation through natural language. It combines coverage scoring, test suggestions, and non-destructive YAML updates in a single MCP server.

Enables AI coding assistants to assess dbt project health and test/documentation coverage, profile warehouse data, detect schema drift, suggest tests, and safely update schema.yml documentation through natural language.

It enables AI assistants to audit dbt project health, profile data, detect schema drift, suggest tests, and auto-generate documentation through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, suggest tests, and safely generate or update schema.yml documentation through natural language.

Enables AI assistants to audit dbt project quality, profile warehouse data, detect schema drift, and auto-generate or update documentation and tests through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, suggest tests, and auto-generate non-destructive schema documentation through natural language.