"YAML" matching MCP servers:

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files.

Enables AI assistants to query, validate, and create vCluster YAML configurations directly from GitHub, supporting version-specific queries and automatic validation.

Provides deterministic conversion between YAML and JSON formats, enabling autonomous agents to flawlessly translate complex configurations without LLM formatting errors.

Enables commit-addressed editing of LinkML YAML schemas via MCP, including applying changesets and reading schemas at specific commits.

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.

Enables AI agents to control and display real-time notifications, telemetry, and custom apps on Ulanzi TC001 and compatible pixel clocks running Awtrix Light, including device management and declarative YAML-based metric polling.

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.

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.

Enables users to determine the Japanese school stage, grade, and subject range for a given knowledge item or term, and to audit LLM explanations for content exceeding a specified grade limit. It also searches normalized MEXT curriculum-code tables and retrieves prerequisite knowledge for topics.

Enables LLMs to perform deterministic arithmetic, high-precision decimal math, unit conversions, statistics, finance, matrix operations, complex-number calculations, numerical calculus, probability distributions, date math, and root solving through MCP tools. It supports both Streamable HTTP and stdio transport.

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.

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.

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.

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.

Enables connecting to multiple ClickHouse instances via dynamic registration or YAML configuration, supporting SQL queries and data source management through MCP tools.

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.

Enables loading and serving markdown files as prompts from local folders or GitHub repositories. Supports automatic repository synchronization and YAML frontmatter for prompt metadata.

Open-source agentic schema layer. Define metrics once in YAML, query governed data from any warehouse (Snowflake, BigQuery, Databricks, PostgreSQL, DuckDB) via MCP.

Enables AI agents to securely transfer files between machines via encrypted, expiring share links, with tools for upload, download, status checks, and link management.

Enables LLMs to interact with the YAMO Blockchain, including reading lesson memories, submitting and retrieving reasoning blocks, and anchoring provenance events.