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    A unified gateway and dashboard that aggregates multiple MCP servers into a single endpoint for streamlined management by AI clients. It features a centralized YAML configuration, a web-based monitoring dashboard, and hot-reload support for managing filesystem, GitHub, and database tools.
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    A declarative framework that manages LLM resources, prompts, and tools through YAML configuration files. It enables LLMs to interact with external data, CLI commands, and Python functions using the Model Context Protocol.
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    19
    MIT
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    An execution state machine MCP server that manages YAML-defined plans, enabling step-by-step execution with state tracking and token optimization.
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    MIT
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    Enables creating no-code agent workflows by combining multiple MCP servers into unified YAML-defined tools. Supports parameterized prompts and scripted steps, exposing a single MCP endpoint that orchestrates external servers like Chrome DevTools and shadcn.
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    10
    MIT
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    Guardrail sidecar proxy between AI agents and their MCP/REST/CLI tools. Policy engine, human approval gates, time-limited grants, rate limiting, and OTEL tracing. One Go binary, one YAML config, fail-closed by default.
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    Apache 2.0
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    * taniwhaai/arai 🦀 🏠 🍎 🪟 🐧 - Policy enforcement for AI coding agents derived from existing instruction files (CLAUDE.md, .cursorrules, .windsurfrules, .github/copilot-instructions.md) — no separate YAML to maintain. Rules with prohibitive predicates (never, forbids, must_not) emit permissionDecision: deny to block tool calls in Claude Code; advisor
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    Apache 2.0