"YAML" matching MCP servers:

Provides semantic search and a tag-based knowledge graph for any project, auto-discovering local markdown knowledge bases with YAML frontmatter.

A Model Context Protocol server for managing prompt templates as markdown files with YAML frontmatter, allowing users and LLMs to easily add, retrieve, and manage prompts.

Minimizes LLM token consumption via YAML serialization, HTML-to-Markdown distillation, and pre-flight token counting, with tools for web content extraction and metrics database querying.

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

A server for the Machine Chat Protocol (MCP) that provides a YAML-based configuration system for LLM applications, allowing users to define resources, tools, and prompts without writing code.

Enables routing of ML tasks like chat, sentiment analysis, recommendations, and summarization to appropriate models through a dynamic YAML-based registry. Provides async FastAPI endpoints with streaming support, retry logic, and pluggable model architecture for scalable ML inference.

Enables the conversion of Dify applications into Model Context Protocol tools, supporting streaming responses and configured via YAML.

Enables an LLM to autonomously maintain a Markdown-based wiki with YAML frontmatter, using hybrid BM25 and local CPU vector search for fully offline retrieval without external APIs or databases.

MCP server that converts .eml email exports (Gmail, Outlook, and other archives) into clean Markdown with YAML front matter — splitting reply threads, stripping signatures and quoted history, extracting attachments, and parsing calendar invites. Built for feeding archived email into RAG and LLM pipelines.

Local-first memory for AI agents: one store shared by Claude Code, Codex CLI, Cursor, Pi, Hermes, OpenClaw, and any MCP client. Plain markdown with YAML frontmatter on your disk, hybrid BM25 + vector recall with per-result provenance (Recall X-ray), a correction contract, and the MemCorrect benchmark.

Provides access to over 500 pre-configured YAML templates and guided workflows for fine-tuning, training, and evaluating LLMs like Llama and DeepSeek. It enables AI assistants to search for recipes, retrieve configurations, and validate parameters for various machine learning tasks.