"Understanding Frontend and Backend Development" matching MCP servers:

MCP server for multimodal understanding and object grounding (bounding boxes) across images, videos, and documents, with support for multiple AI providers (Zhipu GLM-V, OpenAI GPT-4o, Anthropic Claude, or any OpenAI-compatible endpoint).

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

Enables frontend navigation and browser automation through a deterministic MCP server, integrating Browser Use and optional code graph hints for coding agents.

Integrates Xiaomi MiMo's multimodal API to enable understanding of images, audio, and video through natural language prompts.

Enables AI assistants to discover, retrieve, and compare official international development indicators from sources such as the World Bank, FAOSTAT, WHO, UNICEF, and IMF, while preserving source identifiers, units, and citations.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

Serves plain-text notes that an Obsidian user has explicitly published for AI over streamable HTTP, with full-text and embedding-based search. MCP clients can read and search those notes and queue suggested edits that the user's own client applies only if the AI rules allow it.

A template for building MCP backends for lab equipment with an embedded Echo example for immediate testing and development.

A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.

Enables AI agents to read Microsoft Fabric lakehouse or warehouse tables, infer keys and relationships, and create or update a GraphQL API item — returning a ready endpoint for Lovable, Cursor, or Claude Code. It also plans and validates schemas and drafts companion artifacts such as semantic models, dbt sources, join paths, and data dictionaries, with dry-run defaults.

MCP server that exposes an email verification tool, returning verdicts of valid, invalid, or risky with reasons.

Enables managing personal expenses through MCP tools for registering and listing gastos, reusing the backend's service layer.

A FastMCP-powered server for accessing curated travel spot data from a PostgreSQL database. It provides tools to retrieve major attractions, supporting sites, and the top ten most popular destinations.

Enables AI agents to interact with both frontend and backend projects by providing file system access and context through the MCP protocol, facilitating full-stack development assistance.

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.

Enables control and management of smart home devices across multiple rooms through specialized MCP servers. Supports lights, thermostats, fans, and ovens with room-specific rules and automatic persistence.

Provides 8 MCP tools for frontend design: load design system, generate components, scaffold pages, review consistency, extract tokens, check WCAG 2.2 AA, test responsiveness, and export to Figma.

Frontend Design Loop MCP enables coding agents to improve web page designs through screenshot-grounded iteration and proof artifacts, making pages materially better.

Provides access to government development, geography and land information data through a FastMCP interface, including data on new building plans processed by the Building Authority.

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.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Reusable MCP server for Python backend development with FastAPI, MongoDB, and Stackraise support.

Provides AI-powered tools for UX/UI review, web component generation, and code refactoring using Google Gemini.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

A specialized toolchain that guides AI agents through a structured 'Atomic Development' workflow for building Python FastAPI and Supabase backends. It manages project scaffolding and enforces dependency-ordered generation of database models, API routes, and tests.

Enables access to Usage and Billing APIs for managing accounts, products, meters, plans, and usage reporting. Supports operations like creating products/plans, reporting usage, and retrieving billing information.

Enables interaction with a restaurant FastAPI backend for menu management, order processing, customer management, and analytics through natural language.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.