One file per folder
Every source file embedded verbatim in fenced code blocks. Large subtrees split into parts. No more dragging files in one by one.
The annoying part
Feeding a project to an AI tool means flattening the tree, skipping binaries and dependencies, watching for leaked keys, and staying under a token budget. IngestMD does all of that in one click, so a whole folder becomes a handful of clean files you can drop straight in.
What it does
Every source file embedded verbatim in fenced code blocks. Large subtrees split into parts. No more dragging files in one by one.
API keys, tokens, and private keys are scrubbed before anything is written, so you never paste a credential into a chat.
Binaries, node_modules, build output, and anything in your .gitignore stay out. Add your own exclude patterns too.
Every bundle shows an approximate token count, so you know the context cost before you load it into a model.
No uploads, no account, no telemetry, no API key. Everything runs on your machine, offline.
Expose any local repo to MCP-capable AI clients as live context, with the same scanning and redaction as the app.
Point IngestMD at your own local LLM server (Ollama or LM Studio) and ask questions about a repo. It builds a token-budgeted context and answers on your machine, no cloud, no API key.
Describe a task and IngestMD assembles exactly the files you need: semantically ranked, dependency-expanded, packed to a token budget, with a signatures-only skeleton fallback. Plus a quality score and a "why each file was included" manifest.
Turn your current git changes into a review-ready bundle: changed files, their tests, and their dependents, behind an AI review prompt. Diff against your working tree or any base branch.
IngestMD clusters a repo into its real subsystems by imports and shared terms, then writes one titled, budget-packed bundle per area. Focused context instead of a flat dump.
Build a cart of bundles, areas, answers, and task or review contexts, then export a tidy set of Markdown with an index. Save collections by name to reuse, or serve them to AI clients over MCP.
Turn PDFs, Word docs, and spreadsheets into clean Markdown. XLSX sheets become tables, DOCX keeps its headings, secrets are redacted like code. Pull Confluence Cloud pages, spaces, or CQL queries straight into Markdown too.
Search your repo by name, extension, glob, language, size, or content (literal, regex, or semantic), then add the matches straight to a collection. Stop dumping whole folders, and hand the AI precisely the files that matter.
A real desktop app
Every screen, shot on a real repository.
How it works
Choose a source project, and optionally a separate output folder.
Toggle redaction and .gitignore, set grouping, add exclude globs.
One click. IngestMD scans once and writes clean Markdown.
Load the .md files into NotebookLM, ChatGPT, or Claude.
For AI clients
IngestMD ships an MCP server over stdio. The
bundle tool is free; Pro adds task_context, diff_context,
discover_areas, and saved collection tools. Point a client like Claude
Desktop at it and load any local repo as live, task-scoped context, with the same scanning and secret
redaction as the app.
{
"mcpServers": {
"ingestmd": {
"command": "/path/to/ingestmd-mcp"
}
}
} Pricing
The free bundler is fully featured and stays that way: safety is never paywalled. Pro unlocks intelligent, task-scoped context. Every download starts with a 3-day full Pro trial, no account and no card. Local-first: unlock later with a license key.
The deterministic bundler, trustworthy on its own.
Intelligent task context: the right files for the job.
Governed context: consistent, reproducible, audited.
Prices shown are introductory and may change. Every tier runs entirely on your machine.
Get it
Free and open to download. The macOS build is signed and notarized by Apple. Windows builds are not yet signed, so on first launch click "More info" then "Run anyway". All releases »