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Lossless Context MCP

by NORTHTEKDevsversion 2.0.0i★ –iRuns on your computer
Registry nameio.github.NORTHTEKDevs/lossless-context-mcp

Install

Overview

Description from the maker (original English)

Agent context flight recorder: restore after compaction, fan-out packs, signed git-bound receipts.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "lossless-context": {
      "command": "npx",
      "args": [
        "-y",
        "lossless-context-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add lossless-context -- npx -y lossless-context-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "lossless-context": {
      "command": "npx",
      "args": [
        "-y",
        "lossless-context-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "lossless-context": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "lossless-context-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account NORTHTEKDevs, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

  • More stars than 0% of the other 1391 servers in this category
  • Published to the registry more recently than 41% of the other 1391 servers in this category
  • Basic checks equal to or better than 100% of the other 1391 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Lossless Context MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.0.0) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

lossless-context-mcp

The flight recorder for your agent's context. A persistent, hook-fed ledger of every file version your coding agent was shown — so a compaction can't destroy the working set, a subagent fleet doesn't pay for the same files N times, and you can *prove* afterward exactly what the model saw.

                      ┌──────────────────────┐

ledger reads ──────▶│ │──▶ RESTORE working set survives /compact (read_file, MCP) │ flight recorder │──▶ PACKS one cache-cached prefix for a fan-out fleet transcript sweeps ─▶│ (content-addressed │──▶ RECEIPTS signed, git-bound "what did it see" (native Read/Edit) │ archive on disk) │──▶ METERING where the read tokens went, in dollars └──────────────────────┘

See it

Compaction destroys your agent's working set. Not anymore — sweep at PreCompact, manifest injected after, blind edit *blocked*, working set restored, edit allowed:

Two agents, one file — air traffic control. The second agent is stopped mid-clobber, with the culprit named:

Every line in these recordings is genuine output from the shipped code — the demos are generated by docs/demo/make-demo.mjs, which drives the real hooks and server and renders what they actually said. Re-run it yourself.

1. Restore: compaction can't destroy your working set anymore

When Claude Code compacts, roughly two-thirds of a typical context is tool output — mostly file contents — and compaction discards it permanently. The model then flails: re-reading files it half-remembers, or worse, guessing at contents it no longer has (#27242 is the ask, at 80 👍).

The flight recorder closes the loop automatically:

  1. PreCompact — the sweep hook parses the session transcript and archives every file the session touched (native Read/Edit/Write included, not just MCP reads), then writes a ranked working-set manifest. Fast enough to sit on the compaction path — one real 14 MB transcript swept in 361 ms; measure yours: node bench/sweep-bench.mjs .
  2. SessionStart(compact) — the inject hook puts a compact manifest back into the fresh context: *"your working set was these 12 files (edited ones first); restore any of them instead of guessing."*
  3. `restore_context` — re-emits the working set from current disk state, budget-capped, annotating any file that changed since the model last saw it.

Cross-*session* memory tools (claude-mem and friends) summarize what happened for the next session. This is the complementary, mid-session layer: file-version-exact recovery of what you were just working on.

2. Blind-edit guard: the recorder as an active safety net

The recorder knows exactly which file versions the model has actually seen this context epoch. A PreToolUse hook uses that to stop the two ugliest edit failures *before the write lands*:

  • Post-compaction guess-edits — the model edits a file it hasn't read since its context was compacted/reset, working from a summary's memory of the content. (The harness's own read-before-edit tracking is not documented to survive compaction, and newer models are allowed to edit unread files at all.) Denied, with a one-line reason the model sees: *read it (or restore_context), then retry.* Self-healing — costs exactly one extra read.
  • Stale-base edits — the file changed on disk since the model read it (another agent, you, a formatter). Content-hash compared, not mtime-guessed. Denied with the same re-read instruction.

Fail-open by construction: any doubt (unparseable transcript, partial reads, files the model itself just edited, oversized files) → the edit proceeds untouched. Disable anytime with LOSSLESS_GUARD=off.

3. Coordination: air traffic control for concurrent agents

Run two agent sessions on one repo and they clobber each other blind: B edits a file A read ten minutes ago; A edits from its stale copy; the merge is garbage and neither notices. Nothing on the market mediates this locally — but the recorder already knows, per session, what each agent holds and edits. v2 makes that knowledge active:

  • Edit-in-flight detection — when one agent process is allowed an edit, it publishes an intent to a local presence plane (~/.lossless-context/presence/, one file per process, no daemon, no locks). Another agent editing the same file seconds later is denied with the culprit named: *"agent session 3f2a91b0 started an edit on this file 12s ago and it may not have landed yet."* Covers sibling subagents of the same session too.
  • Cross-session stale-base detection — when another session's landed edit postdates what your session holds and your last contact left no verifiable hash, the edit is denied with a re-read instruction. When the ordinary drift check fires, the reason now *names who changed the file*.
  • The radar — coordination_status shows every visible agent session, what it's been editing, and which files have cross-session or in-flight activity.

Honest limits: advisory, not locking. Sessions without the hooks are invisible, a same-second race can still slip through, and presence files are unauthenticated local JSON — any local process could fabricate one to cause false *denies* (structurally never a false allow; presence can only add deny classes). An intent whose edit is then declined at the permission prompt lingers up to LOSSLESS_COORD_INTENT_SECS (90 s) before expiring. It substantially narrows the concurrent-clobber window; it cannot close it, and how much it catches in practice is not yet measured. LOSSLESS_COORD=off disables coordination independently of the guard. (The guard's compaction tracking is session-scoped; only the dedup engine uses the machine-global epoch file, where a cross-session bump merely costs one conservative full re-send.)

4. Packs: stop paying for the same files in every subagent

Fan-outs are where token waste actually lives. Measured across 195 real multi-agent runs: 19.6% of all subagent `Read` tokens were duplicate reads of identical content by sibling agents (5.18M of 26.4M tokens) — every sibling starts cold and reads the same CLAUDE.md, the same spec, the same core modules.

export_pack ranks the archive's cross-session read history for stable hot files and renders them as one deterministic block for a custom agent-type's system prompt. The block is a stable prefix, so the whole fleet hits the provider prompt cache on it. On a real review fan-out this measured 46.55% cheaper than baseline — and the same pack injected per-task measured 19.6% *worse* (every sibling cache-writes it), which is why the tool tells you where to put it. Both numbers were measured externally on one real corpus, not by a harness in this repo — exact figures, method, and that caveat are in BENCHMARK.md. Generate the pack once per run and embed it verbatim: ranking follows live read history, so repeated export_pack calls can differ.

5. Blame: what did the agent see when it did that?

context_blame (also a CLI: lossless-context-mcp blame ) answers the debugging question every agent incident report wishes it could: for a given file, every content version the model was shown (SHA-256 + git blob SHA-1, first/last seen, capture source, sessions) and what else was in context around a chosen moment. When an agent produces a wrong change, you query the recording instead of arguing with the agent's self-report.

6. Receipts: prove what the model saw, bound to git

Observability vendors capture what your agent read into mutable trace stores. Nobody signs it or binds it to repo identity — and agent self-reports are not evidence (ask anyone whose agent claimed it "verified" something it never read).

context_receipt issues an HMAC-SHA256-signed attestation: every file/view shown, the SHA-256 of every content version, the git blob SHA-1 of each version (so any verifier with a clone can check git cat-file -e — was this ever committed?), repo HEAD at issue time, delivery kinds, token totals, and an explicit coverage statement of which capture paths it attests (mcp, and transcript-sweep with include_sweep). By default it signs with the same key file as trust-mcp receipts, so one key verifies a full evidence chain: *what the agent saw + what it did*.

The honest scope: a receipt attests what passed through the ledger and sweeps — it never claims coverage of unmediated paths, and says so in its own coverage.note.

7. Metering (and the token-saver reality check)

context_stats shows where the session's file-read tokens went — per repo, per file, in dollars, counted with a real tokenizer.

Reality check, kept from earlier versions because it's true: as an intra-session

token saver this measures ~0% on real Claude Code transcripts (+0.4% with the

never-lose engine; the native file-state cache already ate the opportunity — full data

in BENCHMARK.md). The savings that DO exist are cross-agent (packs,

above). Read-path dedup remains because it is provably lossless and never negative —

not because it will save you much on its own.

Install (two commands)

npm i -g lossless-context-mcp

lossless-context-mcp init # wires all hooks into ~/.claude/settings.json claude mcp add lossless-context --scope user -- lossless-context-mcp

init is idempotent (re-run it after upgrades — it updates paths instead of duplicating), backs up your settings file first, refuses to touch a settings file it can't parse, and never removes hooks that aren't its own. --dry-run previews. It wires:

  • sweep-transcript.mjs (PreCompact + SessionEnd) — captures the working set + exact versions; bumps the dedup epoch on PreCompact; never blocks compaction.
  • inject-manifest.mjs (SessionStart, matcher compact) — injects the recovered working-set manifest after a compaction.
  • reset-epoch.mjs (SessionStart) — keeps read dedup lossless across new sessions.
  • guard-edit.mjs (PreToolUse, Edit|Write|MultiEdit) — the blind-edit guard (LOSSLESS_GUARD=off disables without unwiring).

Restart Claude Code after init. Without the hooks everything still works — you just lose automatic native-tool capture, post-compaction injection, and the guard; the ledger then records MCP reads only.

Tools

ToolWhat it does
read_file(path, symbol?, lines?, force_full?)Lossless read: full / unchanged-marker / diff. Optional single-symbol or lines:"40-90" views. Refuses binary files.
read_files(paths[], force_full?)A working set in one call; per-file errors don't fail the batch.
working_set(limit?)Heat-ranked table of what the recorder knows this session (+ last 24h), with staleness vs disk.
restore_context(files?, budget_tokens?)Re-emit the working set after compaction — manifest top-K by default, budget-capped, change-annotated.
export_pack(repo?, top?, budget_tokens?, days?)Deterministic fan-out context pack from cross-session read history, for an agent-type system prompt.
context_blame(path, at?, window_minutes?)Forensics: every version of a file the model was shown, plus co-context around a moment. Also: lossless-context-mcp blame from the shell.
coordination_status()The radar: visible agent sessions, their recent edits, cross-session and in-flight files.
outline(path)Cheap structural map of a file (declarations only).

| context_stats() | Token/dollar b

Only the beginning is shown; read the rest in the repo

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