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"Markdown" matching MCP servers:

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    Enables querying and updating Markdown frontmatter metadata using DuckDB SQL, with optional semantic search capabilities for finding similar documents based on content.
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    MIT
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    A universal resource server that enables AI models to mount local folders, scrape web pages into Markdown, and query SQLite databases without writing code. It features a real-time TUI dashboard and persistent configuration for managing diverse knowledge sources.
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    A TypeScript MCP server for interacting with CDISC Define-XML 2.0 and 2.1 files, enabling users to load, search, and retrieve clinical data metadata including datasets, variables, and codelists. It supports local file processing with document caching and provides responses in both JSON and Markdown formats.
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    Integrates Redshift database query capabilities with vector-based knowledgebase tools for semantic search and RAG applications. It enables users to execute SQL queries, explore database schemas, and perform hybrid semantic searches on markdown files stored in S3.
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    MIT
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    Celp-MCP enables natural-language analytics over SQL, MongoDB, and Databricks warehouses through MCP-compatible clients, converting questions into multi-step database plans and returning markdown reports.
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    MIT
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    A graph-friendly relational server that stores project memory, tasks, and metadata in a centralized SQLite database as the authoritative source of truth. It enables AI agents to manage complex project states through entity-relationship modeling and can generate human-readable markdown views on demand.
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    Enables querying and formatting Loki logs from Grafana via the Model Context Protocol. It supports LogQL queries, label retrieval, and provides results in text, JSON, or markdown formats.
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    MIT
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    Enterprise MCP server that exposes multiple MySQL databases to AI services through one endpoint, with API-key auth, per-key database scoping, a layered SQL guard, and a markdown schema-knowledge graph.
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    MIT
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    Zero-config knowledge base for AI coding agents. Loads your markdown docs into a searchable database and exposes them as MCP tools — search, read, and manage documentation without leaving your editor. Works instantly with SQLite (no setup), upgrades to PostgreSQL + pgvector for hybrid semantic search. 6 MCP tools, 3 resources, FTS5 keyword search, 176 tests.
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    MIT
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    An MCP server that provides data bridging from Spring Boot interfaces and a lightweight Python sandbox for script execution. It enables agents to fetch data as Markdown or Parquet files and perform automated data analysis within a controlled environment.
    MIT
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    Enables users to document data and connect it to AI agents by defining tools and instructions in markdown files. It supports building RAG and text-to-SQL applications that can be deployed as MCP servers, APIs, or CLIs.
    5
    MIT
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    Enables LLMs to query a Markdown-based homelab asset database for device information, including searching by name, VLAN, or category.
    MIT
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    Enables LLMs to manage file-based knowledge bases with dual storage (Markdown + SQLite). Supports creating, searching, and organizing articles across multiple knowledge bases with full-text search capabilities.
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    MIT
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    A local-first MCP server that captures working memories and returns compact capsules classified by trust, with consolidation to Datacron markdown notes.
    Apache 2.0
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    Enables Claude to create and organize markdown research notes directly in your local vimango SQLite databases, with support for contexts, folders, and automatic synchronization.
    MIT
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    Exposes SQLite database query tools and markdown document resources over JSON-RPC 2.0 stdio transport, enabling AI assistants to read and search documents and execute read-only SQL queries.
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    MIT
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    Enables AI assistants to interact with Databricks workspaces by hosting secure MCP prompts and tools on Databricks Apps. It allows users to execute Python-based tools using the Databricks SDK and manage prompts through simple markdown files.