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

An MCP server that integrates AI agents with Figma, enabling reading and programmatic modification of designs.

Enables AI agents to directly control Figma Desktop via MCP, supporting UI creation, editing, prototyping, and variable management with over 60 tools.

Enables AI agents to read Figma designs and modify them programmatically, including creating and styling elements, editing text, managing layout and components, exporting images, and working with annotations and prototype connections.

Enables AI assistants to read and modify Figma designs programmatically through the Model Context Protocol.

An MCP server that demonstrates the Model Context Protocol using a mock SQLite database of an online store, offering tools for product search, sales analysis, and reviews, along with resources and prompts for learning MCP integration.

A server providing tools for querying and analyzing Teradata databases, including database management, data quality assessment, and SQL execution capabilities through an MCP interface.

Provides read-only access to live Hylo protocol data on Solana mainnet, including hyUSD/xSOL/eHYUSD mint supplies, pause flags, Earn Pool NAV and pool holdings, and an address's direct SPL token balances for those mints. It answers queries and explains the core mechanism without connecting a wallet, building transactions, or signing anything.

Provides an institutional research backend for AI assistants, with 15 tools for company, financial, funding, competitor, industry, and news intelligence, plus Markdown/PDF report generation, featuring deterministic source routing, extraction, validation, and citation generation.

A Model Context Protocol server for the Quire project management platform, enabling AI assistants to securely interact with Quire projects, tasks, and data.

Enables persistent, auditable business operations state by storing knowledge, evidence, decisions, tasks, execution receipts, and review results in a local SQLite database, so different sessions can continue the same project.

MCP server connecting Claude/Cursor to Codex CLI, enabling code analysis via @ file references, multi-turn conversations, sandboxed edits, and structured change mode.

Connects AI assistants to a Redmine instance over the Redmine REST API, enabling issue, project, user, time-tracking, wiki, file, search, and saved-query operations. Sign-in uses browser-based OAuth 2.0 with PKCE (or an API key for older servers), and the tool catalogue spans 55 tools across 9 groups.

Enables Codex to interact with the user's local OmniFocus database on macOS, allowing task management, project creation, and tag operations through natural language.

A Model Context Protocol server that enables AI assistants to interact with MongoDB Atlas resources through natural language, supporting database operations and Atlas management functions.

Exposes an Oracle database to MCP clients over stdio, offering 75 tools to query, modify, inspect, and triage the database with zero-install thin mode requiring no Oracle Client. Every statement is recorded in a Markdown audit log.

A universal MCP server enabling AI assistants to query and manage six database engines (Postgres, Redis, Elasticsearch, MySQL, MongoDB, LDAP) through 113+ tools with read-only safety and fault tolerance.

A flexible proxy server that aggregates multiple backend MCP servers into a single interface using STDIO or SSE transports. It supports dynamic server management via an HTTP API and utilizes namespacing to prevent tool conflicts across connected services.

A SitecoreMCP version that can be used in enterprises

An MCP server that connects AI assistants to a Neo4j graph database, enabling querying of movie data through tools like graph statistics, genre-based search, and movie details, along with advanced features such as LLM sampling and completions.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

A local-first, no-API-key MCP server that enables LLMs to search the web, fetch pages, and read documents using multiple engines and smart fallbacks.

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

Deterministic encode, decode, hash and convert for agents, on the same pipeline engine as the String Utility Belt web app, CLI and editor extensions: a pipeline built and checked in the browser runs unchanged from its share link. Base64/hex/URL, JWT, hashes, JSON/YAML/CSV/XML, dates and regex; tools to search, describe and run utilities or whole pipelines, and to detect input formats. Runs locally

Enables AI agents to prepare, preview, quote, and send approved faxes, track delivery and evidence, read inbound faxes, extract data, and request human approvals through a fax API.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

Enables LLMs to interact with the AT Protocol ecosystem, including Bluesky, through natural language. Supports public data access without authentication and full write operations with authentication.

Enables AI assistants to read, search, and traverse your entire reMarkable library, including handwritten notes via OCR.

A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.