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"YAML" matching MCP servers:

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    Provides deterministic conversion between YAML and JSON formats, enabling autonomous agents to flawlessly translate complex configurations without LLM formatting errors.
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    MCP server that lets coding agents test AI agents. Create YAML test cases, snapshot golden baselines, check for regressions, and generate visual reports all from inside Claude Code or any MCP-compatible tool. Works with LangGraph, CrewAI, OpenAI, Claude, Mistral, and any HTTP API.
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    Apache 2.0
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    Persistent project context for Google Gemini. 12 MCP tools for .faf Project DNA — auto-detect your stack, validate, score, and sync across CLAUDE.md, GEMINI.md, and AGENTS.md. Python/FastMCP. IANA-registered format (application/vnd.faf+yaml). 183 tests. One file, every AI platform.
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    MIT
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    Security, cost, and health governance proxy for MCP infrastructure. Enforces YAML-configurable security policies (blocklists, rate limits, token budgets), tracks real token costs via tiktoken, monitors server health with live JSON-RPC probes. Features OAuth 2.1/OIDC with RBAC, web dashboard, payload normalization, semantic shell AST analysis, mTLS, and a formal STRIDE threat model.
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    MIT
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    Enables AI assistants to navigate and query hierarchical documentation structures, supporting markdown files with YAML metadata and OpenAPI 3.x specifications. It features intelligent full-text search, metadata filtering, and a built-in web interface for both human and AI-driven documentation access.
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    MIT
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    A Model Context Protocol server that exposes project-specific development tools (tests, linters, typecheckers) to coding assistants through a simple YAML configuration, eliminating the need for manual command approval.
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    A production-ready MCP server template that enables developers to quickly build and deploy MCP servers with dynamic tool/resource loading, YAML-based prompts, and seamless OpenShift deployment. Supports both local development with hot-reload and production HTTP deployment with optional JWT authentication.
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    Enables AI assistants to manage tasks through YAML-based storage with subtask suggestions, status updates, and Mermaid Gantt chart generation. Supports hierarchical task structures with attributes like dependencies, milestones, and parallel execution.
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    ISC
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    Policy enforcement gateway for MCP tool calls, evaluating every tool invocation against declarative YAML policies (allow/deny/escalate-to-human), generating cryptographic hash-chained audit receipts, and including built-in content safety scanning.
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    MIT
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    Open-source agentic schema layer. Define metrics once in YAML, query governed data from any warehouse (Snowflake, BigQuery, Databricks, PostgreSQL, DuckDB) via MCP.
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    Apache 2.0
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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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    MCP server for the OrionBelt Semantic Layer. Enables LLMs to explore governed semantic models, compile YAML metric definitions into optimized SQL across 8 engines (BigQuery, ClickHouse, Databricks, Dremio, DuckDB, MySQL, PostgreSQL, Snowflake), and execute analytics queries via natural language. Works with Claude, Cursor, Windsurf, and Copilot.
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    Apache 2.0
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    An open-source MCP server that protects AI agents at runtime by evaluating every tool call against YAML policies and generating audit trails.
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    Apache 2.0
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    Enables AI-assisted music composition through copyable pattern templates, style constraints, and arrangement tools that compile to MIDI files. Provides 30+ tools for managing musical structures, layers, patterns, and styles with deterministic compilation from YAML arrangements.
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    MIT
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    Retrieves architectural information from ArchiMate models, enabling AI coding assistants to access architectural context during the software development lifecycle. Supports search and retrieval of views and elements in markdown, JSON, or YAML.
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    9
    MIT