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"Information about Microsoft SQL" matching MCP connectors:

Matching Connector Tools:

  • Ask Personio Recruiting the recruiting-ops questions dashboards miss by connecting applications, stage transitions, candidates, recruiting jobs, categories, org units, workplaces, jobs catalog, webhooks, event activity, and intake documents. Find stage-movement stalls, candidate freshness gaps, source quality by job/category, hiring load by department and workplace, webhook delivery issues, intake readiness gaps, and bottleneck owners. No dashboard build. No SQL.

  • Scans text for personally identifiable information — emails, phone numbers, SSNs, credit card numbers, physical addresses, names — and returns a redacted version. Built for agents sanitizing user content, support tickets, logs, or documents before storage, sharing, or feeding into another LLM call. Pay-per-call via x402 (USDC on Base): $0.01/call, no account or API key. tools/list and /openapi.json are free for discovery.

  • AI access to Quadratic spreadsheets: open files, run Python/SQL, query connected databases.

  • Self-hosted, open-source MCP gateway: turn any API, database or MCP server into custom connectors for Claude, ChatGPT, Gemini, Copilot & Cursor — no code. Converts REST, SOAP, WSDL, GraphQL & SQL to MCP, with OAuth2, RBAC & audit log. 175+ pre-built adapters. This is the public read-only demo endpoint — run your own at https://github.com/HelpCode-ai/anythingmcp

  • Query and join across SaaS tools, SQL, and NoSQL databases through one unified SQL interface.

  • AI data intelligence platform that brings your data warehouse, dashboards, monitoring, and accumulated business knowledge into any MCP client. Connects to BigQuery, Snowflake, PostgreSQL, MySQL, ClickHouse, Redshift, Databricks, SQL Server, and Azure Synapse.

  • Connect your AI assistants to Keboola and expose your data, transformations, SQL queries, ...

  • The all-in-one data stack for agents. Upload files, run SQL, evolve tables, and render charts.

  • A collaborative substrate over your data: vector, knowledge graph, SQL, geospatial, streaming.

  • The grounded data layer for any LLM: governed SQL, metrics, lineage and catalog over your data.