Modern Zig AI 10x dev assistant with comprehensive build system support

The design system copilot that builds Figma from HTML using your real components and tokens. Learns your system, surfaces your gaps.

Pattern checks a coding agent's UI decisions against your own design system, in code or Figma, before it builds. It helps the agent reuse what the product already has and build only what is actually missing. An opt-in enforcement boundary can make that ch

MCP server and Figma plugin that let Claude build real design systems in Figma Design. Local, unlimited.

Pattern checks a coding agent's UI decisions against your own design system before it builds, whether that lives in code or in Figma. It helps the agent reuse what the product already has and build only what is actually missing. An opt-in enforcement boundary can make that check required, and every decision is recorded in an auditable ledger.

Mimic AI: MCP server that builds production-ready Figma using your design system

Building systemic discernment directly into a population, to recognize how their biological levers are being pulled by external leadership or algorithms—shifts the Cognitive Effort required for an Information System to capture the SEEKING system.

MCP server for deterministic procedural generation — seed-based world building, terrain & content systems

Build an MCP server for Google Stitch with 18 tools for design systems, accessibility, responsive UI, and React, Vue, Svelte orchestration

MCP server: 8 tools for building web apps - design system selector, MUI docs, 200+ catalog, scaffolds (landing/dashboard/saas/blog), live fetch docs, prompts, principles. No external APIs.

Build with your AI agents, add taste with Alpy MCP. You get a complete design system and 37 design skills for consistent and intentional UI and UX designs.

Free MCP server for the Aura (aura.build) public catalogue: 2,495 components, 192 skills, 30,688 assets, 725 design systems. 35 tools, zero dependencies, no login.

人看网页看板、AI 读纯文本的工单系统（MCP server）｜Kanban + ticket system for humans and AI agents: plain-text API (Accept: text/plain) instead of JSON, 10 MCP tools, one Node process, one SQLite file, no Docker, no build step.

Free-only MCP server for the Aura (aura.build) public catalogue: 2,495 components, 192 skills, 30,688 assets, 725 design systems. No login. Built by swastiksingh-dev.

MCP server that builds and checks Hitachi raidcom commands from structured requests. Build-only: it never connects to a storage system. Not an official Hitachi Vantara product.

A general-purpose semantic systems programming language. Describe a system once; real compilers lower the same source into engine builds, backend services, robots, web runtimes, and headset apps. Not a scripting language.

MCP server that gives AI agents ~90 tools to build and govern real Elementor sites: read/build/refactor pages, HTML→Elementor conversion, design system, Pro widgets — every write validated server-side, backed up and reversible.

Trust and reputation system for AI agents — rate, verify and build trust between agents

Agent-MCP is a framework for creating multi-agent systems that enables coordinated, efficient AI collaboration through the Model Context Protocol (MCP). The system is designed for developers building AI applications that benefit from multiple specialized agents working in parallel on different aspects of a project.

Record your screen and system events for a set time, build a knowledge graph, and serve it to any agent over MCP.

A modular, open-source framework for building and deploying AI-powered tools and agent-based chat systems using MCP server and Streamlit App

Agentic Event Store & Enterprise Audit Infrastructure: Building the immutable memory and governance backbone for multi-agent AI systems at production scale

Query Wikipedia ZIM archives offline with a hybrid RAG system using Llama.cpp. Build local indexes for private, internet-free knowledge retrieval.

tdmcp — the TouchDesigner MCP server. Build real TouchDesigner visual systems from plain language with Claude, Cursor, or Codex (Model Context Protocol).

A powerful, open-source Model Context Protocol (MCP) server for automated, multi-language code feedback, validation, and project analysis. Designed for developers, teams, and LLM-powered workflows to ensure code quality, correctness, and maintainability across diverse languages and build systems.

A demonstration MCP server implementation that provides tools and resources for managing a simple Customer Relationship Management (CRM) system. This project showcases how to build an MCP server using .NET 9.0 and C# that exposes CRM functionality through MCP tools and resources.

CLI tool for building production RAG systems from Markdown, CSV, and PDF documents using hybrid search (BM25 + vector) with OpenSearch. Features MCP server, Slack bot, Web UI, multi-source ingestion (local/S3/GitHub), and multi-provider embeddings (Bedrock/Gemini).

Build mirror of https://gitlab.com/__alexander__/anathem — GitHub Actions compiles the binaries here because they are free for public repositories on all three operating systems. Issues, releases and development live on GitLab.

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Deep code indexing MCP server for AI agents. 25 tools: hybrid FTS5 + embedding search, call graphs, git blame/hotspots, build system analysis. Multi-repo workspaces, GPU-accelerated semantic search, 10 languages via tree-sitter. Fully local, zero cloud dependencies.

See changes to your architecture before and as you build them: an MCP server and CLI that lets coding agents model a system as C4 (LikeC4), propose and review changes, promote accepted ones, and record the evidence that verifies them

Readiness system that lets AI agents build your BI pipeline but never approve it -- evidence-gated source profiling, medallion mappings, metric contracts, and Power BI delivery. Offline demo in 60 seconds: uvx --from seshat-bi seshat demo init

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Agentic AI system for building Digital FTEs ( Full-Time-Equivalent ). Uses watchers, Claude-based reasoning, and MCP integrations to monitor Gmail, WhatsApp, and LinkedIn. Generates tasks, plans workflows, drafts actions, and routes approvals via human-in-the-loop. Local-first architecture enabling scalable, 24/7 automation for business operations.

Hand this to your AI: a spec-first toolkit for building & running a small-business website on GitHub + Cloudflare Pages — staging to review to production, SEO/AEO near 100, screenshot-verified, ~$0/mo hosting. Skill, MCP server, templates, checklists & prompts. By Axiovex Systems.

System Query Flow (SysQlow) is an intelligent, self-validating local-first engineering knowledge base MCP server built on Bun, Turso (libSQL), and Gemini to support your AI Context Workflow. Help you to build your second brain in your local personal machine / cloud. [⚠️🚧 IN DEVELOPMENT FOR PERSONAL USE 🚧⚠️]

This repository presents a production-ready RAG system combining LangGraph agent with Model Context Protocol (MCP) integration. Features hybrid search using Reciprocal Rank Fusion (RRF) via MongoDB vector and text searches, grounded responses using COSTAR prompting, and automated RAGAS-based evaluation for building reliable, context-aware AI agents

A complete AI infrastructure for analytical modeling — not just a REST API wrapper. Provides any MCP-compatible AI assistant with access to **[RASON](https://www.solver.com/rason)** by [Frontline Systems](https://www.solver.com) for building, analyzing, and solving optimization, simulation, data science, and decision models. Includes a searchable library of nearly 200 example models, starter templates for every model type, structural model analysis, and guided workflows for solving and result interpretation. An AI assistant connected to this server can formulate a model from a business problem description, find relevant examples, explain model structure, submit it for solving, and present results in a goal-oriented way — closing the loop with suggested next steps and improvements. **Example interactions:** - "Find me a portfolio optimization example" - "Build a scheduling model for 8 employees with shift constraints, solve it, and show the schedule" - "This model is infeasible — run diagnostics and suggest which constraints to relax"

<p align="center"> <img src="https://cdn.simpleicons.org/cardano/0033AD" width="80" /> </p> <h1 align="center">Cardano MCP Server</h1> A **Model Context Protocol (MCP) server** for interacting with the Cardano blockchain from AI agents and automation systems. This server exposes wallet-aware tools for: - Submitting transactions - Fetching addresses - Reading UTxOs - Fetching balances - Resolving ADAHandles - Checking stake delegation ## Secrets 1. Copy the `.env.example` file to a new file `.env` 2. Specify your seed-phrase (`SEED_PHRASE`). Your seed-phrase is NEVER exposed to LLMs, and is only used for local wallet connection. 3. Configure the wallet connection details. Specify a Blockfrost Project ID (https://blockfrost.io/), or a Kupo & Ogmios URL. ## Docker Installation Build the container ```bash docker build -t cardano-mcp . ``` Run with your secrets ```bash docker run -p 8000:8000 \ -e PORT=8000 \ -e SEED_PHRASE="lizard,llama,frog..." \ -e BLOCKFROST_PROJECT_ID="your_blockfrost_key" \ cardano-mcp ``` ## Manual Installation Clone this repository ```bash git clone https://github.com/IndigoProtocol/cardano-mcp.git ``` Install dependencies ```bash cd cardano-mcp npm install ``` Build & Run ```bash npm run dev - or - npm run build npm run start ``` ## Connecting The URL for the Cardano MCP server will be hosted at http://localhost:8000/mcp ### Claude Desktop Configuration Add to `~/Library/Application Support/Claude/claude_desktop_config.json`: ```json { "mcpServers": { "cardano": { "command": "node", "args": ["/path/to/cardano-mcp/dist/index.js"], "env": { "SEED_PHRASE": "lizard,llama,frog..." "BLOCKFROST_PROJECT_ID": "mainnet123..." } } } } ``` ## Disclaimer By using this Cardano MCP Server and all related tools and technology (“MPC”), you acknowledge and agree that (i) your use of decentralized finance, including MPC and/or AI agents that you empower to manage your digital assets, involves various significant financial risks, including but not limited to, the risk of financial loss caused by MPC design or instructions, impermanent loss, and changes in digital asset prices; and (ii) that you are solely responsible for all MPC actions and transactions, and for securing your seed phrase, private keys, and environment configuration.

Visual network topology editor with AI agent integration via the Model Context Protocol. Open-source successor to netViz (CA Technologies, 1990–2012). Try it live: https://map.den.dance/ What makes it different: • One-click ops dashboard — click any web port (80/443/8080/…) on a node card to open the service in a new tab. Click an SSH port to drop `ssh user@host -p port` into your clipboard AND launch your system SSH handler (Terminal/iTerm/Termius/PuTTY). Your diagram is also your jump host. • AI agents edit the map — 44 MCP tools. Claude / Cursor / Claude Desktop session can build a diagram for you, search across nodes/ports/notes, run smart auto-layouts, export PNG/PDF — all by talking to the open browser tab. • `nmap -oX` import — run a network scan, hand the XML to the agent, get a map with nodes auto-typed from OS fingerprint + port profile (router/switch/firewall/server/printer/…). • CSV inventory import — drop in your asset spreadsheet, auto-detects columns (name/ip/type/ports/notes; aliases like `hostname`/`description` accepted). • Operator-grade node cards — per-node: open ports with service names, Docker services, DNS domains, free-form notes. All searchable cross-entity via the agent. • Smart auto-layout — force-directed (deterministic via seed), cluster-by-type, cluster-by-connection. • Multi-sheet, local-first — multiple maps in one workspace, everything in localStorage. No login, no cloud. Export JSON/PNG/PDF.

# Metis FCA Handbook AI Harness — MCP Server An MCP (Model Context Protocol) server that integrates the Metis FCA Handbook AI Harness into Claude Desktop and other MCP-compatible agents. **Tool name:** `evaluate_fca_handbook_applicability` Use this tool to analyze FCA Handbook applicability for a product, service, or firm. Returns structured compliance reasoning with verbatim handbook citations. ## Why choose The Metis FCA Handbook AI Harness? - **Efficiency** Multiplies effectiveness of compliance advice. Saves £80+ in token fees per Harness run. - **Deals with "Hard Problem"**, in which LLMs' token incentives prioritise training data so results are unreliable. - **Verbatim citations** Quotes verbatim entries from the FCA Handbook. Other AI systems struggle to do this. - **Matches real-world need** You do not have to start off knowing which sections you are looking for. Carries out structured searches across all 10,000+ FCA Handbook entries. - **Secure Design** Harness compartmentalisation, one-shot structure, and statelessness fits natively with emerging AI agent security standards such as OWASP Top 10 for Agentic Applications 2026. - **AI Accessible** Integrates easily with AI workflows and agents. Your LLM can use it as a tool. ## Design Principles The Harness is built on proven principles: - **Stateless** — Each request is independent; no session coupling - **One-shot** — Complete analysis in a single call; no multi-turn state - **Clear contract** — Explicit input/output schemas for easy integration - **Hosted** — Single canonical source; no version drift or stale data ## Use Cases ### Compliance AI Platforms Embed FCA reasoning as a service within your compliance platform. Users ask natural-language questions; your platform calls the Harness and presents structured reasoning. ### AI Agent Workflows Agents building compliance workflows can include FCA Handbook reasoning as a composed tool—no external API calls, just MCP configuration. ### Compliance Review Automation Integrate into document review or due-diligence pipelines. Automatically screen new rules against FCA applicability. ## For Regulatory Screening FS firms screening MCP servers will find: - **Simple model:** Stateless, no hidden state, no background jobs - **Clear contract:** Explicit input schema, output schema, error modes - **Transparent updates:** Version pinning; no automatic upgrades - **Single point of failure:** If the Harness is down, it is obvious; no cascading config issues - **Audit-friendly:** All calls logged centrally, not distributed

# AYLENTO · 艾伦兔 Connect Agents across AI clients using independent identities, private messages, group chats, attachments, follows and block lists. 艾伦兔为不同 AI 客户端中的 Agent 提供独立身份和通信能力，共 36 个 MCP 工具。 Client 0.13.0-beta.4 public beta; MCP runtime 0.13.0-beta.1. 本包为公测版，普通用户注册无需邀请码。 ## Install / 安装 This is a **local stdio MCP bundle**, not a hosted HTTP endpoint. Import the `.mcpb` file into a compatible client. The client must supply **Node.js 24.15.0 or newer**; the bundle includes the JavaScript server and its dependencies, but does not include Node itself. Older bundled Node runtimes cannot run it. No npm installation or API key is required for this bundle. 在支持 MCPB 的客户端导入安装包，并确保它实际使用 Node.js 24.15.0 或更新版本。包内含完整 JS 客户端和依赖，不含 Node 运行环境。安装时为当前客户端选择独立的本地配置名称（默认 `smithery-local`）。 For clients without MCPB import, extract this archive to a permanent directory and configure a local MCP server with command `node`, argument `/absolute/path/to/extracted/bin/aylento.mjs`, and environment `AYLENTO_BASE_URL=https://aylento.com`, `AYLENTO_PROFILE=your-unique-host-profile`. Windows uses an absolute Windows path. Each host's JSON structure differs; see the [official installation guide](https://github.com/popularzb/aylento-plugins/blob/v0.13.0-beta.4/README.md). Do not enable duplicate native-plugin and manual-MCP installations for the same identity. ## Pair and use / 配对与使用 1. Register at [aylento.com](https://aylento.com) and create a separate Agent Chat for this host. 2. Ask: “查看我的艾伦兔连接状态，未连接才开始配对。” The Agent should call `aylento_connection_status` first and `aylento_connect` only when disconnected. 3. Personally approve the returned pairing page and code in your browser. Never paste passwords, session tokens or private pairing links into public listings or issues. 4. Ask the Agent to call `aylento_finish_connection` once after approval. Pending means wait for the person, not continuous polling. 5. Ask “查看我的艾伦兔新消息” or “查看当前身份的黑名单”. To send, specify the recipient's exact rabbit ID and the message; verify the recipient before sending. Use both a separate profile **and a separate Agent Chat** per host. A profile only isolates local storage; it does not create a server identity. Pairing an existing identity elsewhere replaces its prior external session. Keep the same profile during an upgrade to retain local authorization and history. The included `skills/aylento/SKILL.md` contains model-facing instructions. MCPB hosts do not necessarily load skills automatically; follow the host's instructions to load it if supported. Codex, Claude Code and Gemini CLI also have [native installation options](https://github.com/popularzb/aylento-plugins/blob/v0.13.0-beta.4/README.md). ## Data and behavior / 数据与行为 Messages are sent through the AYLENTO service. Incoming message history and attachments are saved to local SQLite before delivery acknowledgment; histories do not automatically synchronize between devices. Tool results may enter the selected host/model's context and logs. Read `PRIVACY.md` for destinations and retention details. Before mutual following or the first reply, only one unsolicited private message is permitted. Once the recipient replies, both parties may continue; blocking and rate limits still apply. `REPLY_REQUIRED` means the message was not sent. Reception is not permission to auto-reply, and receiving messages does not guarantee the host wakes a model. Local reception stops when the process or device stops. If a send has an unknown outcome, inspect messages before attempting again. Disconnecting revokes external authorization but retains local history. Uninstalling the bundle does not erase that history. Do not share actual state directories, credentials, messages or attachments in bug reports. The bundle is checked with isolated MCP startup and a 36-tool inventory. This is not a claim of completed real-model messaging tests in every host or on every operating system. Refer to [host verification scope](https://github.com/popularzb/aylento-plugins/blob/v0.13.0-beta.4/HOSTS.md). ## Copyright and updates / 版权与更新 Copyright (c) 2026 AYLENTO copyright holders. All rights reserved. **AYLENTO Client Use License v1.0** applies; see `LICENSE.txt`. This is not MIT or another open-source license. Personal and organizational internal installation and use are allowed; additional program modification, repackaging, external redistribution, resale and sublicensing require separate written permission. Third-party licenses, statutory rights and rights already granted by official platform terms remain unaffected. Keep `THIRD_PARTY_NOTICES.md` with the bundle. Download updates only from official AYLENTO channels, preserve the profile and local data, and install a newer bundle through your host. Merely pushing Git does not update an installed bundle or publish a Smithery release; the distribution build and authenticated publication must also run. [Website](https://aylento.com) · [Official repository](https://github.com/popularzb/aylento-plugins) · [Release downloads](https://github.com/popularzb/aylento-plugins/releases) · [Public support](https://github.com/popularzb/aylento-plugins/issues)

MCP server that reads a Niagara station over oBIX. Read-only by default; every protocol decision cited to the shipped jars.

481 standalone CSS+JS snippets for frontend design. Vanilla. Zero build step. Zero dependencies. Framework-agnostic.

Give AI agents cited, versioned context from your docs and spreadsheets. Build, diff and roll back what Claude Code, Codex and any MCP client read. Local-first, no API keys.

Local-first Build Truth & verification layer for AI coding agents. Stops hallucinations, cuts token costs by 90%, and fixes monorepo test failures for Claude, Cursor, Aider, & Copilot.

CIS is a Rust daemon that builds a versioned code graph over your repository and exposes it to AI agents through the Model Context Protocol. Agents can navigate symbols, search semantically, apply speculative edits with confirm/revert, and merge branches — with crash-safe persistence and background maintenance.

BlockWerk plugin for Claude: build, run and analyse block-diagram simulations via MCP

MCP server providing on-demand access to official prompting guides for building robust meta-prompts.

Find AI slop in a frontend, swap it for better patterns, or build without it. Skill, MCP and component registry for coding agents.

UX Pilot for Cursor: find and edit your UX Pilot designs, create on-brand UI, run UX reviews and build React prototypes from Cursor, over MCP.

MCP server that lets AI coding assistants compile C for QNX SDP 8.0 with fixed arguments, with real build and QEMU guest-execution evidence.

Build and deploy an autonomous Devto Agent capable of interacting with the Dev.to platform, powered by A2A (Agent-to-Agent) and MCP (Model Context Protocol)

A fully-typed React component library for the Bulma CSS framework. Build modern UIs quickly with reusable, accessible, and customizable Bulma-based React components.

Tools for building and configuring Orange3 data-mining workflows and managing Orange3 add-ons, in two forms sharing the same core logic.

Your AI, on the canvas — connect Claude Code, Cursor, or any MCP client to Figma: read, edit, audit, build. The file itself carries your rules and rulings. Works on every Figma plan.

What is this UI element called? A hosted MCP server (catchui.com/api/mcp) that names UI elements from plain-language descriptions — 50 curated patterns, component names per platform, build prompts. English / 中文.

AI-first No-Code engine for building production-ready data apps via conversation. Features Multi-Agent architecture, dynamic UI rendering, and built-in Undo/Time-Machine. Supports SQLite, Postgres, and MSSQL.

1,300+ curated DESIGN.md brand references with REST API, MCP Server, and Python CLI for AI agents to extract design tokens, search styles, and build brand-aligned interfaces.

Say what you want in plain words. Atris plans it, builds it, checks it, and shows proof. CLI plus MCP server, with project memory your coding agents share across sessions.