"PostgreSQL MCP (Multi-Version Concurrency Control or specific usage)" matching MCP servers:

Read-only MCP server for extracting data across multiple CallTrackingMetrics sub-accounts using a single parent API key. Enables cross-account call data, reports, numbers, contacts, and configuration analysis for resellers and agencies via Claude.

Enables ingestion of HL7 v2, ASTM, and JSON control results, Westgard multirule evaluation, automatic patient-result holds, explainable root-cause investigation, and supervisor-approved CAPA workflows for clinical laboratories. Agents can also establish targets, compute Levey-Jennings and sigma statistics, and verify corrective actions before a supervisor lifts a hold.

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.

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

Enables querying and mutating data across multiple sources (CSV, Google Sheets, SQLite, PostgreSQL, MySQL, MongoDB, Oracle) through a dynamic GraphQL API, with flexible filtering, batching, persisted queries, and dynamic source switching.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

Exposes a DuckDB-backed MCP server that lets an agent fetch a dataset's schema and run read-only SQL queries against it. All execution is filtered server-side, enforcing single-statement, read-only safety independent of the calling model's behavior.

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.

Enables an AI assistant to answer customer-success questions by synthesizing simulated billing, support ticket, and product usage data into composite churn-risk scores, bands, and plain-language explanations for fictional accounts. It exposes raw-data tools, a synthesis tool, and a methodology resource so users can list at-risk accounts or get a per-account health summary.

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.

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.

Provides AI assistants with semantic layer visibility and multi-dimensional querying capabilities over Cube.js data.

An MCP server for the Octopus Energy Japan GraphQL API that allows users to retrieve electricity consumption, cost estimates, and contract details. It enables 30-minute interval usage analysis and postal code area searches while ensuring privacy by excluding personal address information.

Enables efficient extraction of specific data from JSON APIs using JSONPath patterns, reducing token usage by up to 99% compared to fetching entire responses. Supports single and batch operations for both JSON extraction and raw text retrieval from URLs.

Enables users to convert structured data into Token-Oriented Object Notation (TOON) to reduce LLM token usage and costs by up to 70%. It provides tools for encoding, decoding, and analyzing data formats like JSON, CSV, and XML to optimize prompt efficiency.

Enables agents to interact with MySQL databases securely, supporting multi-database read-only queries, protected DML writes, and streaming exports to Sandbox Datasets.

Converts JSON data to TOON (Token-Oriented Object Notation) format and back, reducing token usage by 30-60% for more efficient LLM applications.

Production-ready MCP server for Apache DolphinScheduler. Enables AI agents to operate data workflows end-to-end with 58 tools covering DAG creation, schedule management, instance control, guided troubleshooting, serial backfills, and version rollback.