"YAML" matching MCP servers:

A Model Context Protocol server that creates tools from API configurations defined in YAML files, allowing easy integration of external APIs into an MCP ecosystem without coding.

A token-efficient, schema-aware MCP server that enables AI assistants to safely read, modify, query, and validate JSON, YAML, and TOML files with automatic schema detection and format conversion capabilities.

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 loading and accessing LAML (YAML-based markup language) documents from the filesystem through the Model Context Protocol.

A declarative framework for building MCP servers using YAML configuration, generating optimized Rust code.

A simple MCP server for utilizing prompts defined in YAML files.

MCP server for lossless YAML 1.2 editing by AI agents. Two tools — noyalib_get (read a value at a dotted/indexed path) and noyalib_set (write a value, preserving comments/formatting byte-for-byte). Pure Rust, streaming-first serde, JSON Schema validation. MIT OR Apache-2.0.

* 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

Provides safe, deterministic inspection, transformation, validation, and diffing of structured data (JSON, CSV, YAML, Parquet) via schema-aware MCP tools.

Parses Unity text-serialized .prefab, .unity, and .asset files to output only Inspector-visible data in hierarchical YAML, reducing token usage by up to 96%.

Enables image generation using Gemini native models, supporting both single prompts and batch processing via a file-based queue. It allows for detailed configuration of aspect ratios and models using YAML frontmatter across various MCP-enabled clients.

Exposes design system component documentation to LLM clients via MCP, allowing listing and retrieving component contexts from Markdown files with YAML frontmatter.

Large-file-safe JSON MCP server with 10 tools: format, validate, search, JSONPath query, deep diff, and multi-format conversion (CSV/TSV/YAML/TypeScript). Handles JSON of any size without crashing.

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

Digital identity layer for AI — your bio, career, skills, interests, and projects always available to every AI tool. Auto-generates profile from 342+ public APIs, 13 real-time plugins, YAML-based profiles with privacy-first local storage.

A convention-aware MCP server for managing markdown files with YAML schema validation. It enables AI agents to read, write, search, and validate markdown notes while enforcing user-defined conventions across directories like Obsidian vaults and documentation repositories.

Enables building LangGraph projects by editing a structured YAML spec, with MCP tools for scaffolding, adding nodes/edges, validating, and deterministically rendering Python graph code.

A Jira-lite MCP server that enables planning-first workflows for AI-assisted development. It manages tickets as markdown files with YAML frontmatter, guiding agents through epics, stories, tasks, and architecture decision records.

Provides AI agents with persistent execution context across sessions through local YAML files, enabling shared memory, task tracking, milestone management, and context hygiene for single or multi-agent workflows.