redcon

Name: redcon
Author: natiixnt

by natiixntversion 1.15.0i★ –iRuns on your computer

Registry nameio.github.natiixnt/redcon

Install

Overview

Description from the maker (original English)

Deterministic context packing for AI coding agents; measured 83% fewer input tokens, local.

PopNx MCP’s three pillars

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

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

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: Other

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

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "redcon": {
      "command": "uvx",
      "args": [
        "redcon",
        "mcp",
        "serve"
      ]
    }
  }
}

Run this in your terminal

claude mcp add redcon -- uvx redcon mcp serve

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "redcon": {
      "command": "uvx",
      "args": [
        "redcon",
        "mcp",
        "serve"
      ]
    }
  }
}

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": {
    "redcon": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "redcon",
        "mcp",
        "serve"
      ]
    }
  }
}

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 natiixnt, 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 Otheri

More stars than 0% of the other 32583 servers in this category
Published to the registry more recently than 21% of the other 32583 servers in this category
Basic checks equal to or better than 100% of the other 32583 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 redcon 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 (1.15.0) 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 Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, sync. labs

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.

Redcon

Deterministic context budgeting for AI coding agents

Stop sending agents 200k tokens of irrelevant code. Redcon scores, compresses, and packs repo context so your agent gets what it actually needs.

Install - Quick Start - How It Works - Docs

The Problem

AI coding agents burn tokens on irrelevant context. You either:

Dump the whole repo and pay for 200k input tokens per request, or
Let the agent grep blindly and waste tool calls figuring out where to look

Redcon solves both. It ranks files by task relevance, compresses them with language-aware strategies (full, snippet, symbol extraction, summary), and packs the result under your token budget. Deterministic, local-first, no embeddings. One MCP server covers Claude Code, Cursor, Windsurf, Cline and Zed; a plain CLI covers CI. Measured on this repository it cuts input tokens by more than 83% at the same task coverage (methodology).

Punch above your plan

On a flat subscription - Claude Pro/Max, Cursor, GitHub Copilot - the token bill isn't what stings, the usage limit is. Redcon cuts the tokens each task needs, so the same plan covers far more work before you hit the weekly wall. Same subscription, more runway.

It stretches the budget you already pay for, and reports exactly how much it saved.

Install

Option 1: VS Code Extension (easiest)

Install Redcon - Context Budget from the marketplace
Open the Redcon sidebar, click Install & Set Up
Reload window. Done.

The extension installs the CLI via pip, registers the MCP server for Claude Code, Cursor, and Windsurf, and gives you a sidebar with budget analytics, file rankings, and compression dashboards.

Option 2: CLI + MCP Server

pip install "redcon[mcp]"

redcon init # creates redcon.toml + registers MCP

The init command auto-configures MCP for Claude Code, Cursor and Windsurf, plus VS Code, Codex CLI, Gemini CLI, Junie CLI, Cline and Zed when they are detected, so your AI agent can call redcon_rank, redcon_search, redcon_compress, and redcon_budget as native tools. It also writes a short AGENTS.md section that tells agents to prefer these tools for context selection.

Option 3: CLI only

pip install redcon

redcon init --no-mcp

Quick Start

# Rank files relevant to a task

redcon plan "add rate limiting to auth API" --repo .

Pack context under a token budget

redcon pack "refactor payment flow" --repo . --max-tokens 30000

Compare compression strategies

redcon benchmark "add caching" --repo .

Audit a PR for context growth

redcon pr-audit --repo . --base origin/main --head HEAD

Output goes to run.json (machine-readable) and run.md (human-readable). Use them in CI, or feed the compressed context directly into your agent.

For a full end-to-end walkthrough on a realistic service repo (plan, pack, validate, with deterministic output), see examples/service-repo.

How It Works

task: "add rate limiting to auth"

| v [1] scan - incremental scan of repo files (cached) | v [2] rank - score each file: keyword match, imports, file role, git history | v [3] compress - per-file strategy: full / snippet / symbol extraction / summary | v [4] pack - fit top-N compressed files under token budget, drop the rest | v run.json + run.md + compressed_context ready for your agent

Every step is deterministic. Same input, same output. No embeddings, no random chunking.

Benchmark: context-eval

Selection quality is measured, not claimed. context-eval/ is an open benchmark for context-selection tools: tasks come from real git commits, ground truth is the files each commit actually modified, and every tool packs the same token budget. Current results (33 tasks, 24k budget):

Tool	Mean coverage	Tokens / coverage point
`redcon`	43.8%	306.8
`keyword-topk` (baseline)	29.8%	538.9
`aider-repomap` (real aider)	15.3%	533.3
`pagerank` (baseline)	11.4%	720.0

Rerun it on any repo: python context-eval/run.py --repo /path/to/repo. Methodology, limitations, and how to add your own tool: context-eval/README.md.

MCP Integration (Pull Model)

mcp-name: io.github.natiixnt/redcon

Instead of pushing a 30k-token blob to your agent, Redcon exposes 9 MCP tools the agent calls on demand:

Tool	What it does
`redcon_rank`	Top-K files with scores and reasons - call this first
`redcon_overview`	Lightweight repo map grouped by directory
`redcon_repo_map`	Top ranked files plus their code signatures, fitted under a token budget
`redcon_compress`	Compressed single-file view for cheap inspection
`redcon_search`	Regex search scoped to ranked files or full repo
`redcon_structural_search`	ast-grep structural search - patterns match the AST, not text
`redcon_budget`	Plan fitting files within a token budget
`redcon_run`	Run a shell command, return its output compressed
`redcon_quality_check`	Run a command and verify the compressed output against the quality harness

Typical agent flow uses ~5k tokens for exploration instead of 30k for a blob. The agent itself decides what to read in full.

Config gets written automatically to:

.mcp.json (Claude Code)
.cursor/mcp.json (Cursor)
~/.codeium/windsurf/mcp_config.json (Windsurf)

Command Output Compression

Source files are only half the bloat. The other half is command output: git diff, pytest, cargo test, grep, ls -R. Redcon's redcon_run MCP tool (and redcon run CLI) wraps the call, parses the output, and returns a budget-aware compressed view that preserves every fact the agent actually needs.

Headline reductions on representative inputs:

Compressor	Fixture	Raw tokens	Compact	Ultra
`git diff`	12 files, 240 hunks	8,078	97.0%	99.5%
`pytest`	30 failures + 200 passes	2,555	73.8%	99.2%
`grep`/`rg`	600 matches across 50 files	7,015	76.9%	99.9%
`find`	500 paths	3,398	81.3%	99.8%
`ls -R`	30 dirs x 15 files	1,543	33.5%	99.0%
`kubectl events`	200-row CrashLoopBackOff	~5,000	91.5%	99.5%
`py-spy collapsed`	200 stacks	2,385	90.0%	99.0%
`json-line log`	200 NDJSON records	6,038	91.1%	98.0%
`coverage report`	50-file grid	738	73.2%	95.0%
`psql EXPLAIN ANALYZE`	11-node Postgres plan	435	71.3%	93.3%

Quality is enforced separately. Every compressor declares must_preserve_patterns (file paths in a diff, failing test names in pytest, branch name in git status, slowest node operator in EXPLAIN); the M8 quality harness rejects any compressor whose compact output drops a fact present in the raw input. Run it as a CI step:

redcon cmd-quality   # exits non-zero if any compressor regressed

redcon cmd-bench # markdown table; --json for CI baselines redcon run "git diff" --quality-floor compact --max-output-tokens 4000

Twenty compressors ship today: git_diff, git_status, git_log, pytest, cargo_test, npm_test (vitest+jest), go_test, grep, ls, tree, find, lint (ruff+mypy), docker, pkg_install (pip+npm+yarn), kubectl_get/kubectl_events, profiler (py-spy+perf), json_log, coverage, sql_explain (Postgres+MySQL TREE), bundle_stats (webpack + esbuild metafiles). Full per-schema benchmarks: docs/benchmarks/cmd/.

Cross-call dimension

Beyond per-call compression, four layers compose across an agent session:

Path aliases (V41): repeated paths like redcon/cmd/pipeline.py collapse to f001 on later mentions. Lazy first-use, never net-negative.
Content reference ledger (V43): paragraph-shaped blocks above 6 cl100k tokens get session-stable {ref:001} aliases on second-and-later occurrences. Empirically 23% of session output had block-level overlap.
Symbol aliases (V49): CamelCase types / multi-word snake_case identifiers (>=8 chars) collapse to c001 aliases the same way paths do. Empirically 72% of distinct symbols recur >=2 times per session.
Snapshot delta vs prior call (V47): when the same argv runs twice, ship only the delta. Schema-aware renderers for pytest (set-diff over failure names), git_diff (file-set with per-file +/- counts), and coverage (per-file pp moves) win meaningfully over generic line-diff. Always picks min(cost_delta, cost_abs) so non-regressive by construction.
Invariant cert (V93): every COMPACT/VERBOSE output stamps mp_sha=<16hex> over the sorted multiset of (pattern, capture) extracted from raw. Auditors recompute the cert against the compressed text to detect spurious additions or capture thinning - upgrades the existing must-preserve boolean to set-equality.

Empirical measurement on 5 simulated agent sessions (benchmarks/measure_sessions.py): the cross-call layers add +8.3% session-level saving on top of the per-call compressors, with +15% on heavy-overlap sessions (debugging, search-and-edit) and near-zero on distinct-content sessions. V85 adversarial GA fuzzer ratchets all 20 schemas as a hard CI gate (REDCON_V85_ENFORCE=1).

VS Code Extension

Once installed you get:

Sidebar chat: type a task, send, watch the pack run live
Dashboard: donut/pie/bar charts for budget, strategies, token impact per file
Status bar: current budget usage with risk indicator
CodeLens: compression strategy and token count shown above each file
File decorations: relevance score badges on files in the explorer
History: browse past runs, diff them, export to clipboard

Branding: red->navy gradient with triple chevron mark, glass-style UI.

Workspaces (Multi-Repo)

One task can span multiple repos. Place the workspace TOML in a folder that contains all the repos (a monorepo root or a common parent directory) - repo paths must resolve inside that folder, so a workspace file cannot reach above itself:

name = "backend-services"

[scan] include_globs = ["**/*.py", "**/*.ts"]

[budget] max_tokens = 28000 top_files = 24

[[repos]] label = "auth-service" path = "auth-service"

[[repos]] label = "billing-service" path = "billing-service" ignore_globs = ["tests/fixtures/**"]

Artifacts include workspace, scanned_repos, selected_repos, and repo-qualified paths like auth-service:src/auth.py.

See docs/workspace.md.

Python API

from redcon import RedconEngine

engine = RedconEngine()

Rank files

plan = engine.plan(task="add user auth", repo=".", top_files=15)

Pack context

result = engine.pack( task="add user auth", repo=".", max_tokens=30000, top_files=25, ) print(f"Used {result'budget'} of {result['max_tokens']} tokens") print(f"Risk: {result'budget'}")

for file in result["compressed_context"]:

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

redcon

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

sync. labs

README from the maker

Redcon

The Problem

Punch above your plan

Install

Option 1: VS Code Extension (easiest)

Option 2: CLI + MCP Server

Option 3: CLI only

Quick Start

Pack context under a token budget

Compare compression strategies

Audit a PR for context growth

How It Works

Benchmark: context-eval

MCP Integration (Pull Model)

Command Output Compression

Cross-call dimension

VS Code Extension

Workspaces (Multi-Repo)

Python API

Rank files

Pack context