"Databases or database systems written in Python, like Postgres" matching MCP servers:

what is go-mcp-postgres? go-mcp-postgres is a Model Context Protocol (MCP) server designed for interacting with Postgres databases, allowing for easy CRUD operations and automation without the need for a Node.js or Python environment.

Enables SQL queries against PostgreSQL databases through MCP-compatible clients and includes a natural language agent for forming SQL queries from natural language.

Open source MCP server specializing in easy, fast, and secure tools for Databases.

The Multi DB MCP Server is a high-performance implementation of the Database Model Context Protocol designed to revolutionize how AI agents interact with databases. Currently supporting MySQL and PostgreSQL databases.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON

MCP server for GLEIF LEI (Legal Entity Identifier) database access. Look up, validate, and search legal entities and their relationships in the global LEI system.

Self-hostable, markdown-native team wiki with a built-in MCP server: agents search, read, and write your wiki pages (ranked Postgres full-text + semantic search, backlink traversal). Plus Atlas, which auto-generates a cited, coverage-checked wiki from your git repos and Jira.

A Bitbucket CLI and MCP server written in Go for managing workspaces, repositories, pull requests, pipelines, issues, and source code. Supports stdio and HTTP transport.

Connect Legacy Databases to AI Agents via Model Context Protocol. Open-source bridge for LLM data analysis.

A TypeScript server implementing the Model Context Protocol that provides secure code execution in isolated Docker containers, allowing LLM applications to safely run Python, Go, or JavaScript code snippets.

Enables AI assistants to manage and query Tiger Cloud database services, including service lifecycle operations, database connections, and SQL execution.

Enables AI assistants to query databases using natural language, with automatic schema discovery and SQL compilation.

Execute code in 8 programming languages via gVisor-sandboxed Docker containers. Supports Python, JS, TS, Go, Java, C++, C, and Bash.

Enterprise-grade MCP Server for FHIR-based EMRs. Enables AI agents to read, search, create, and update any FHIR R4 resource across major EHR systems like EPIC, Cerner, and OpenEMR.

ToolMesh is an Apache-2.0, self-hosted MCP gateway written in Go that sits between AI agents and backend systems. It enforces a fail-closed pipeline on every tool call, including per-tool and per-user authorization, server-side credential injection, structured audit logging, and output policies. APIs are declared in YAML with DADL, turning REST endpoints into MCP tools without writing a custom MCP

The official Hostim MCP server, built into the hostim CLI. An agent can deploy a Docker image or a Git repository, create managed Postgres, MySQL and Redis, add volumes and custom domains, and read app status, logs and events. Hostim runs on EU bare metal in Falkenstein, Germany. Read-only by default; tools that create, change or delete need --allow-write.

Official MCP server for the Nhost backend platform. Manage Postgres data, GraphQL, auth, storage, migrations and Hasura metadata via AI assistants.

Enables AI agents to securely interact with FHIR R4-based EMRs, supporting read, search, create, and update operations on any FHIR resource type across systems like Epic, Cerner, and OpenEMR.

Enables AI agents and workflows to safely explore and query data in ephemeral sandboxed databases via MCP, with guardrails and snapshot capabilities.