"How to create Markdown files locally" matching MCP servers:

An MCP server that gives AI agents structured read/write access to a story-based project backlog. Agents can list stories, read content, update status, and append notes — all backed by plain markdown files that live inside your project repository. There is no shared server. The backlog files live in your repo under requirements/, committed and versioned alongside your code

Manages an agent's long-term memories as versioned markdown files with searchable SQLite indexes, supporting lifecycle states, conflict-gated recall, revision, and explicit forgetting without automatic deletion.

Local-first shared memory and task coordination for AI coding agents. One Go binary, MCP server, markdown files you own. Hooks for Claude Code and Codex CLI (and their desktop apps).

Local MCP server for plain Markdown notes: list, read, create and search notes, and manage their tasks and Kanban board (move cards, toggle tasks, set start and due dates).

Local harness for Codex, Claude Code, opencode and other coding agents: agents record pitfalls and design decisions as Markdown files reviewed in the same PR as the code, and route only task-related records to the next agent.

MCP governance server that lets AI agents build and maintain a persistent, versioned wiki of interlinked Markdown files through validated tools, enforcing invariants like linking, immutability, and one git commit per write.

LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.

Provides git-aware safe file operations, allowing AI agents to delete files while protecting git-tracked source code from accidental deletion.

Enables AI coding assistants to search and retrieve information from a locally ingested knowledge base using hybrid search, grounded in user-curated documentation.

Enables publishing self-contained HTML files or directories to get short, readable links, with an MCP tool for agents to create and manage previews.

An MCP server to create secure code sandbox environment for executing code within Docker containers.

Enables AI assistants to structurally understand a Moodle installation and its plugins, so they can answer codebase questions and scaffold plugins that follow existing conventions. It scans Moodle sources and generates Markdown context files, indexes, and MCP tools/resources/prompts for querying, diagnosing, and maintaining that understanding.

Enables analyzing sales and stock CSV files to identify what to reorder, how much to buy, when products will run out, and how much inventory is tied up in dead stock. Provides read-only inventory checks and CSV column inspection without uploading data.

Records how any command-line tool behaves (flags, stdin, outputs, exit codes, edge cases) as golden tests, then diffs a Rust or Go rewrite against the original to prove it behaves the same.

Provides AI agents with typed tools to bootstrap sessions, ingest files, search, read, write, link, handoff, self-check, and audit markdown notes in a self-hosted Obsidian-compatible vault.

The Multi DB MCP Server is a high-performance implementation of the Database Model Context Protocol designed to revolutionize how AI agents interact with databases. Currently supporting MySQL and PostgreSQL databases.

Converts URL content to Markdown, providing two tools: one for extracting main article content and one for converting the entire HTML.

Enables MCP clients to read articles, preview Markdown output, and manage publishing tasks across supported platforms.

Enables AI agents to create, manage, and track tasks within plans using Valkey as the persistence layer. Supports plan and task management with Markdown notes, status tracking, and prioritization through multiple transport protocols (SSE, Streamable HTTP, STDIO).

Enables AI assistants to search PatchXNote records, inspect AI-generated results, create Markdown drafts, and send user-approved messages to webhooks while keeping server data read-only.

Enables MCP clients to create and live-update Excalidraw diagrams in batches, with preview links, checkpoint restoration, browser editing, and optional authenticated shared storage.

An MCP server providing web search, image search, and page scraping tools to LLMs without requiring API keys.

Records your terminal sessions per command (PTY + OSC 133) into local SQLite, so AI agents can search, retrieve, and diff what commands actually printed. Secret redaction is applied by default to everything served over MCP.

Agentic DevOps control loop and MCP server featuring polyglot Tree-sitter AST blast-radius analysis, OPA Rego policy evaluation, and isolated --network=none Docker test verification[cite: 1]. Exposes tools to inspect impacted dependency closures, validate security guardrails, and run targeted test suites.

Model Context Protocol server for the NetLoc8 IP geolocation API. Gives AI assistants access to country/city lookup, timezone reconciliation, and IP utilities.

PCI DSS v4.0.1 compliance scanner for Go payment services, delivered as an MCP server. Every finding maps to a specific PCI DSS requirement ID. Taint-aware cardholder data flow analysis with PCI SSC FAQ semantics, designed to complement broad SAST tools like Semgrep and CodeQL - not replace them.

Turn any OpenAPI/Swagger/Postman spec into an MCP server — give your LLM live access to any API.

Self-hostable remote terminal assistance over MCP. Agents create one-command connection invitations, execute commands, and manage interactive sessions on user-authorized macOS, Linux, and Windows computers without requiring an SSH daemon.

CNCF release intelligence: typed, quoted facts (security fixes, breaking changes, deprecations) from every graduated/incubating project's release notes, updated daily. check_stack compares running versions locally — versions never leave your process.

MCP server that exposes any LynxPrompt instance to LLMs, enabling browsing, searching, and managing AI configuration blueprints and prompt hierarchies.