"Connecting a backend project to an Elasticsearch database" matching MCP servers:

MCP server providing backend access to PostgreSQL, Storage (Supabase/S3), Iceberg data lake, and SQL seeds. It offers 32 tools for database queries, storage operations, seed management, and more.

Read/write Elasticsearch mcp server with many tools

Connects agents to Elasticsearch data using the Model Context Protocol, allowing natural language interaction with Elasticsearch indices through MCP Clients like Claude Desktop and Cursor.

This is an elasticsearch mcp server developed based on sprint ai.

Enables AI agents to perform read-only static analysis of Node.js backend projects, detecting database query anti-patterns, async bottlenecks, connection pooling mistakes, and dependency hygiene issues while returning structured evidence-backed findings.

A comprehensive MCP toolkit for Java backend developers, providing 35 tools across 5 servers for database analysis, JVM diagnostics, migration assistance, Spring Boot monitoring, and Redis diagnostics.

Enables coding agents to load backend anti-slop rules and deterministically audit TypeScript/backend files for violations like SELECT *, SQL interpolation, N+1 calls, silent catches, stack leaks, type escapes, and TODOs, plus verify TypeScript compilation. Returns PASS/FAIL findings so agents can fix issues before shipping.

Provides a natural language interface for querying and managing PostgreSQL, MySQL, MariaDB, MSSQL, and SQLite databases using the Model Context Protocol. Users can explore database schemas and visualize query results through an integrated web dashboard.

MCP server for connecting to databases (PostgreSQL, MySQL, SQL Server, Redis) enabling SQL queries, table exploration, and Redis key-value operations.

A local, read-only MCP server that analyzes Python backend projects by providing tools to scan, map, and selectively read files, reducing token usage for AI clients.

A universal, architecture-agnostic blueprint for building Model Context Protocol servers, providing a modular structure with components for domain logic, storage, security, export, ingestion, forensics, and MCP protocol.

Enables structured dynamic analysis of authorized Windows processes via Cheat Engine 7.7, exposing bounded tools for process inspection and debugging without arbitrary code execution or memory writes.

Exposes workspace-scoped Backend project queries via MCP, enabling explicit token pairing, status checks, and scoped sync of project docs, source artifacts, graph data, and evidence metadata without background synchronization.

Read-only MySQL/MariaDB MCP server for running SELECT queries safely, with automatic read-only enforcement and query limits.

Project Memory stores decisions, evidence, outcomes and reviewed lessons in local SQLite. Assistants retrieve bounded context through three MCP tools. People follow and plan work in a live workspace with an overview, sprint board, decision history, readable documents, skills and project maps. Offline exports remain available.

A cross-project knowledge graph for MCP that maps features across mobile, backend, and admin codebases, giving AI agents full-stack context.

Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.

Enables SQL database interaction with true server-side result paging, runtime-managed connection profiles, and SSH-bridged access for PostgreSQL.

MCP server for querying and managing multiple databases (SQLite, PostgreSQL, MySQL) with read-only mode and schema inspection.

Intent-based project documentation server that automatically maps natural language queries to the right sources (plans, todos, roadmap, docs) and provides comprehensive task management with dependency tracking, backlog promotion, and archival workflows.