"Assistance with mapping and refining a PostgreSQL database" matching MCP servers:

Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.

Loads CSV files into a temporary SQLite database and provides comprehensive data analysis tools via MCP, enabling AI assistants to query, analyze, and export data using natural language.

MCP server providing a single query tool to execute SQL against GetDot Database, enabling schema discovery, data loading, and querying via natural language.

Enables AI agents to discover official public data sources, inspect their metadata and endpoint parameters, and execute live queries through a unified response envelope with a 500 ms budget.

Enables AI agents to securely interact with multiple databases (MySQL, PostgreSQL) via natural language queries, with cross-database querying and enterprise-grade security.

A server that connects to PostgreSQL databases and provides tools for safely exploring schemas, running read-only SQL queries, and performing data analysis with pre-built templates.

An MCP server that enables advanced CSV analysis and data visualization using Google's Gemini AI and Plotly. It allows users to perform exploratory data analysis, generate interactive charts, and conduct complex reasoning on tabular data.

基于Model Context Protocol (MCP) 的A股实时行情查询服务器，支持查询A股实时价格、历史K线数据、财务信息及市场概况。

A versatile tool that enables querying and exporting data from multiple relational databases (MySQL, PostgreSQL, Oracle, SQLite, etc.) in read-only mode for data safety.

Enables AI agents to interact with PostgreSQL databases through the Model Context Protocol, providing database schema exploration, table structure inspection, and SQL query execution capabilities.

Enables AI to interact with Microsoft Access (.mdb, .accdb) and SQLite 3 databases, including read-only support for Access 97 databases. Supports executing SQL queries, importing/export data from CSV and Excel files, and storing notes about database files.

Enables LLMs to interact deeply with PostgreSQL databases—query data, manage schema, analyze performance, and administer the database.

A Model Context Protocol server that provides tools for interacting with databases, including PostgreSQL, DuckDB, and Google Cloud Storage Parquet files.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Enables users to look up how specific v2 fields, datatypes, and table codes translate to R4, and to convert whole ORM, ADT, and ORU messages into transaction Bundles with explicit warnings for unmapped values.

Give your AI agent the entire China A-share market: 55 datasets of prices, financials, capital flows, corporate events and futures/options, stored locally and queryable with read-only SQL. Built point-in-time with delisted stocks kept, so backtests don't lie — no account, no API key.

Enables users to convert a vendor partner directory (such as PartnerPage or the Shopify Partner Directory) or a trade body member list into one company row per listing, each carrying website, domain, tier, tags, and location for Clay-ready output. It runs the extraction on Apify, returning dataset rows for up to 20 directory URLs with optional deduping, detail enrichment, and a cost cap.

Minimalistic MCP server that lets AI assistants inspect, quality-check, and clean CSV datasets through tools, resources, and prompts, without needing local file access.

Enables agent tools to validate queries, assess token costs, and extract structured datasets from unstructured text using multiple LLM providers.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Exposes local DuckDB pizza sales data as MCP tools for querying store-level and total sales metrics via natural language, enabling local LLMs to perform real-time analytical queries.

Provides stock market data and computes technical indicators like EMA, MACD, and RSI across multiple timeframes using the LongPort API and TA-Lib. It enables users to perform detailed financial analysis and multi-timeframe market reviews through standard MCP tools.

Enables users to preprocess, analyze, and visualize CSV data through comprehensive tools for data manipulation, statistical analysis, and graph generation.

Lets an agent query JSON files too large for its context: JSONPath with aggregation pipes, schema, field profiles, samples, identity-keyed diff, CSV and spreadsheet export, and checks that gate a file. Int64 IDs and decimals keep their exact digits, and zstd, Base64-Zstd and PostgreSQL bytea payloads open directly.

An MCP server that provides safe, read-only access to a PostgreSQL warehouse, enabling schema introspection, guarded SQL queries, and truncated result reporting for LLMs.

Read-only SQL across PostgreSQL, MySQL, S3-compatible buckets and folders of Parquet, CSV and JSON files — and one query can join across all of them, federated in-process by DuckDB. Sources are given as connection strings at startup, and no tool writes: they are absent rather than disabled.

MCP server for Hydra ETL: lets AI assistants write declarative YAML ETL pipelines (CSV, Parquet, PostgreSQL, MySQL, MongoDB, APIs) and validate them before any data is read or written.

Enables executing SQL queries, exploring database schemas, and exporting results from Redash via MCP, with ad-hoc queries and write protection.

This MCP server enables database migration between heterogeneous systems (Oracle, PostgreSQL, SQL Server, Netezza to PostgreSQL or SQL Server) through AI assistants by providing tools for command preview, execution, validation, and workflow suggestions.

It gives AI agents a ranked, token-budgeted digest of what changed since their last cursor, with drill-down handles and cursor acknowledgement, read from a local database of file and SQL source changes. It enables agents to act on current business state without re-reading and diffing everything themselves.