"Information about SQL" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Check Supabase SQL for who can read and write each table: RLS gaps, open policies, grants, fix SQL.
Query Postgres, MySQL, SQL Server, Oracle, BigQuery, ClickHouse and Redshift from your AI client.
Ask your PostgreSQL, MySQL or Odoo data in plain words from Claude, ChatGPT or any MCP app. Read-only: STYD runs safe queries and answers with the real number, the period and the definition it used. No SQL needed. OAuth sign-in, sample data to try right away, and every answer names its workspace so data from different companies is never mixed.
Database schema inference, dialect normalization, query plan optimization, and SQL audit over streamable HTTP.
Database guardrails for AI agents: check SQL against your Vericto rules before it runs.
Query your own databases and Data Sets with read-only SQL, and build hosted dashboards.
Every spreadsheet is a table: Excel, CSV, Parquet and JSON as SQL tables for AI agents.
Data Oil is a multi-model database built for object storage. It uses a log-structured storage engine, snapshot isolation with optimistic commit, and a change feed from the write-ahead log. Its SQL dialect supports joins, window functions, CTEs, set operations, graph traversal and pattern matching. It has its own HNSW vector index and BM25 full-text index, and a write is searchable by keyword and vector as soon as it commits. Access is over HTTP, the PostgreSQL wire protocol (extended protocol). We have a founder's program available for projects which may be of interest https://dataoil.tech/founders.html if your interested lets chat ;)
Every spreadsheet is a table: upload Excel, CSV, Parquet or JSON files and query them in SQL (Apache DataFusion), with joins across formats, schema and column statistics. Hosted tableski server; connect with your tableski.io access token. Open source: github.com/yarenty/tableski
Ask Claude, ChatGPT, or any MCP client about your game's players, retention, revenue, and win rates.
ParseHub.dev is a hosted document-to-records service with a remote MCP server. Agents can upload a CSV, PDF, or Word file, summarize and analyze CSV data (filter, sort, group_by, and more), read text from PDF and Word files, extract attendees, agenda items, and action items from meeting notes, ask a question about an uploaded document, and store files as database records to list and read later. Each account sees only its own files and records. **Endpoint:** `https://parsehub.dev/mcp` (Streamable HTTP, stateless) **Auth:** API key in the `X-API-Key` header (keys start with `mcp_`), or `Authorization: Bearer <API key or JWT>`. Every request without credentials returns 401. **Plans:** Free (100 API requests/month, 1 document upload), or 5,000 requests/month on the $9/month or $42/year plan. Every MCP request counts toward the allowance. **Tools:** upload_csv, summarize_csv, analyze_csv, upload_document, read_document, extract_meeting_notes, ask_document, database_status, list_database_documents, get_database_document, store_document, extract_stored_meeting_notes, export_database (administrators only). Not affiliated with ParseHub (parsehub.com), the web-scraping tool. ParseHub.dev does not scrape websites. By Poietes LLC. Docs: https://parsehub.dev/guide#connect-via-mcp
Hosted SQL Server MCP server for Claude, ChatGPT and Cursor, with each action switched per account.
Backend brasileiro (Postgres, login, storage): a IA cria tabelas, liga RLS e roda SQL no projeto.
Connect your AI to any database — PostgreSQL, MySQL, or SQL Server — in seconds.
DBRE-grade SQL analysis inside any MCP client. No connection. No install. Paste a query.
ER diagrams from SQL, Prisma or ORM models. One tool, no account, nothing stored.
Turns SQL CREATE TABLE statements into an entity relationship diagram you can print.
SQL validation, formatting, parsing, metadata extraction, linting, and security analysis.
Search ILOSTAT labour indicators, query and compare series, build country profiles, run SQL.
Access control for text-to-SQL agents. Before the model sees the schema, schemagate keeps only the tables and columns the caller may read, picks the few a question needs, and refuses hidden ones at query time. This public endpoint serves a sample schema (no real data). Self-hosted it works with Postgres, Oracle, MySQL, SQL Server and SQLite.
Push CSV, TSV, JSON, JSONL or Parquet from your agent and get a link a human can explore: filters, column charts, SQL, pinned findings and what changed since the last push. Viewing is free, no signup; anonymous datasets live 7 days. Tools: push, pin_finding, get_dataset, list_datasets, delete_dataset, get_limits. Docs: https://rows.page/docs/mcp
Query a TikTok data warehouse (creators, videos, sounds, daily history) with read-only SQL.
Supabase-compatible Postgres for agents: projects, branches, SQL, auth, storage, email.
Read-only MyDankTank catalog, Journal, policy, and machine metadata service for adults 21+.
Every figure re-checked against SQL-computed facts; unsupported questions refused with the reason.
Deterministic safety, correctness & cost gate that vets Postgres SQL before your AI agent runs it.
Executes SQL in a real ephemeral database: rows, typed errors with suggestions, plans, diffs.
AI access to Quadratic spreadsheets: open files, run Python/SQL, query connected databases.
Your Supabase account in natural language: run SQL, apply migrations, manage tables, storage, edge f
Your Databricks Lakehouse in natural language: run SQL on your SQL warehouses, track long-running qu
