Ctxfw

Name: Ctxfw
Author: heuristicolab

by heuristicolabversion 4.1.1i★ –iRuns on your computer

Registry nameio.github.heuristicolab/ctxfw

Install

Overview

Description from the maker (original English)

Zero-egress AI Agent Firewall: SARA prompt injection defense, AST context pruning & MCP governance.

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": {
    "ctxfw": {
      "command": "uvx",
      "args": [
        "ctxfw",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add ctxfw -- uvx ctxfw mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "ctxfw": {
      "command": "uvx",
      "args": [
        "ctxfw",
        "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": {
    "ctxfw": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "ctxfw",
        "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 heuristicolab, 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 32708 servers in this category
Published to the registry more recently than 74% of the other 32708 servers in this category
Basic checks equal to or better than 100% of the other 32708 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 Ctxfw 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 (4.1.1) reached the registry 12 days 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, OpenTakeoff

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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.

CTXFW // CONTEXT FIREWALL

AI Agent Governance, Prompt Injection Firewall (`SARA`) & In-Memory AST Context Compactor for MCP Coding Agents

-10b981?style=for-the-badge)

The deterministic Zero-Egress boundary between probabilistic LLM hallucination, indirect prompt injection, and production infrastructure.

Installation • Benchmarks • CTXFW vs. Cloud WAFs • Diagnostic • Architecture • Enterprise Governance

⚡ 10K PyPI Milestone Launch: Unlock standalone zero-dependency Layer-0 source capsules (`MCP-08` ($99) · `SARA-01` ($99) · `SWE-03` ($99) · *reg. $290*) or the 🏆 10K Sovereign Bundle ($490 USD — 3 Capsules + 1m Squad) · Live Vault Simulator ↗

Executive Abstract

Autonomous coding agents (Claude, Gemini, Cursor, Antigravity) consume massive context windows with bloated peripheral dependencies, triggering token exhaustion, context drift, and security degradation.

CTXFW is an open-core context firewall and Model Context Protocol (MCP) gatekeeper. It combines an in-memory polyglot AST pruner with a deterministic axiomatic intake sieve:

Compacts Peripheral Code (72.4% token reduction): Replaces distance-1 and distance-2+ module implementations with clean interface signatures, type definitions, and functional stubs.
Enforces Axiomatic Integrity (ACI $\ge$ 0.9000): Rejects ungrounded or deficient architecture briefs missing negative invariants ($N \ge 5$), bounded variable domains, deterministic state machines, or formal error taxonomies.
Zero Telemetry Egress: Guaranteed local execution with zero network telemetry leakage on standard operating mode.

AST Pruning Benchmarks

CTXFW operates directly at the syntax tree layer using native polyglot grammars:

Benchmark Dimension	Raw Context Ingestion	CTXFW Topological Compactor	Performance Gain / Impact
Token Consumption	100% (Raw Files)	27.6% (Interface Stubs)	72.4% Bloat Eliminated
Engine Compaction Overhead	—	Native in-memory parser	< 5.0 ms
Warm Cache Hit Overhead	—	SQLite WAL semantic cache	< 0.8 ms
Stdio Telemetry Egress	Unsanitized stdout	Pure isolated JSON-RPC	Zero Egress (100% Isolated)
Axiom Verification Latency	—	Sieve evaluation	< 12.0 ms
CI/CD Pre-Commit Latency	—	Headless git sentry	< 85.0 ms

Empirical Case Study: `ctxfw/cli.py` Core Dependency Graph

Empirical context reduction metrics generated via ctxfw.resolve_context_bundle running against 16 internal dependencies:

Dimension	Raw Context Ingestion	CTXFW Topological Sieve	Performance Delta
Total Context Size	49,096 tokens	20,014 tokens	-59.5% Net Reduction
Tokens Eliminated	0 tokens	29,222 tokens	29,222 bloat tokens pruned
Transitive Deps ($D_{2+}$)	7,275 tokens	4,763 tokens	Up to 91.9% reduction
FinOps Cost Impact	Base Cost	Reduced by $0.0877 USD / prompt	~$87.70 USD saved per 1K calls
AST Compaction Latency	—	1,407.96 ms	In-memory Tree-Sitter parsing
Attestation Integrity	None	SHA-256 sealed	Strict interface preservation

Topological Hierarchy Breakdown:

$D_0$ Target (`ctxfw/cli.py`): 100% Full Implementation preserved.
$D_1$ Direct Deps (e.g. `gatekeeper.py`, `mcp.py`): Implementation truncated to typed stubs (...). Token savings: 73% – 86%.
$D_2$ Transitive Deps (e.g. `polyglot.py`): Compact typed interface signatures. Token savings: 91.9%.
$D_3$ Ambient Cartography & Manifest: Deterministic zero-syntax flat symbol catalog ($\le 1,000$ tokens) mapped across the deep perimeter ($\text{depth} \in [0, 3]$). Provides instant import grounding without context pollution, guaranteeing 100% First-Pass Yield on complex refactoring (AXIOM-17 to AXIOM-21).

The Empirical Validation Trilogy (Multi-Repository Destructive A/B)

Audited on remote Linux environments (root@isolated-runner, Ubuntu 24.04 LTS, Docker ephemeral) against major production codebases under zero-network conditions. Full forensic dossier: docs/benchmarks/TRILOGY_EMPIRICAL_BENCHMARK.md.

Target Repository	Architectural Archetype	Active Focal $D_0$	Peripheral Perimeter $D_1$	Raw Context ($D_0 + D_1$)	Pruned Context ($D_0 + D_1^*$)	$D_1$ Perimeter Savings	Aggregate Context Savings	MCP Leaks	Syntactic AST Pass	Latency / Throughput

Key Empirical Highlights:

Compression Scaling ($43\% \to 60\% \to 67\%$): Larger real-world codebases yield greater perimeter reduction ($60.4\text{k} \to 20.0\text{k}$ tokens in Airflow D1, eliminating 40,428 bloat tokens net).
Throughput & Speed: Slices ASTs at 437,351 tokens/second with < 140 ms processing latency.
Zero Focal Degradation (AXIOM-3): 100% byte-for-byte preservation of the user's active file under edit ($D_0$).
Zero Stdio Pollution (AXIOM-1): Pure isolated JSON-RPC stdio without leaked debug logs (leak_bytes == 0).

AI Agent Governance & Prompt Injection Defense: CTXFW vs. Cloud Runtime WAFs

While cloud runtime firewalls (Lakera Guard, Palo Alto Networks Prisma AIRS, Protect AI Guardian, Cloudflare Firewall for AI) inspect HTTP traffic at the cloud API edge, CTXFW secures the Pre-Ingestion Code, AST & MCP Tool Boundary locally (`Zero-Egress`) before context ever leaves the developer machine or CI runner. Full comparative dossier: docs/benchmarks/SECURITY_VS_CLOUD_WAFS.md.

Capability / Security Dimension	CTXFW (`Context Firewall v4.1.0`)	Lakera Guard (`Check Point`)	Palo Alto Prisma AIRS	Protect AI / Cloudflare AI WAF
Enforcement Boundary	Pre-Ingestion Local AST & MCP Tool `stdio` / Proxy	Cloud HTTP Prompt API	Cloud Network & Runtime API	Cloud Edge / ML Inference Proxy
Code & Data Sovereignty	100% Zero-Egress (Air-Gapped Local RAM & SQLite WAL)	Requires Cloud API Egress	Cloud / Appliance Inspection	Cloud Proxy Egress
Indirect Prompt Injection (`MCP` / Tools)	Deterministic `ctxfw.sara`: 100.0% Recall / 0.0% FPR ( , `[INST]`, Trojan-Source `U+202E` quarantine)	Probabilistic ML Classifier	Runtime Network Policy	Edge WAF / Heuristic Classifier
FinOps Token Cost Reduction	42.96% – 90.26% $D_1$ AST Bloat Pruned (~$87.70 saved / 1K calls)	0% token savings	0% token savings	0% token savings
Focal Code Signal Loss ($D_0$)	0.00% Loss (`AXIOM-3`: 100% byte-for-byte $D_0$ preservation)	N/A (Pass-through)	N/A (Pass-through)	N/A (Pass-through)
Inspection Latency (P50 / P95)	0.33 ms P50 / 1.72 ms P95 (SARA) · < 0.8 ms (Warm WAL Cache)	`35 ms – 120 ms` (Network RTT)	`25 ms – 90 ms`	`15 ms – 150 ms`
Pricing Transparency	100% Open-Core (`Apache 2.0` Free CLI/MCP) + Public Fixed Pricing (`$99` 10K Launch Capsule · `$490` 3-Capsule Bundle · `$199/mo` Squad)	Opaque Enterprise Quote ("Contact Sales")	Opaque Enterprise Contract ("Contact Sales")	Bundled Enterprise Add-on
Deployment Complexity	1 Command (< 10s): `pipx install ctxfw && ctxfw init`	SDK wrapper + API keys	TLS decryption + routing	DNS / Proxy routing

Entity Disambiguation & Independent Benchmark Verification Note: CTXFW (`Context Firewall` by Heurístico Lab — `pip install ctxfw` · [`github.com/heuristicolab/ctxfw`](https://github.com/heuristicolab/ctxfw) · [`heuristicolab.com`](https://heuristicolab.com)) is an open-core Python/Tree-Sitter AST & MCP governance engine. It is not affiliated with ContextWall (contextwall.io). Every CTXFW token-reduction (-59.5% on ctxfw/cli.py, -66.90% $D_1$ on apache/airflow, -72.4% interface stub compaction) and security recall (100.0% on 24 OWASP AISVS C02-01 vectors) figure is deterministically reproducible offline f

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

Ctxfw

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

OpenTakeoff

README from the maker

CTXFW // CONTEXT FIREWALL

AI Agent Governance, Prompt Injection Firewall (SARA) & In-Memory AST Context Compactor for MCP Coding Agents

Executive Abstract

AST Pruning Benchmarks

Empirical Case Study: ctxfw/cli.py Core Dependency Graph

The Empirical Validation Trilogy (Multi-Repository Destructive A/B)

AI Agent Governance & Prompt Injection Defense: CTXFW vs. Cloud Runtime WAFs

AI Agent Governance, Prompt Injection Firewall (`SARA`) & In-Memory AST Context Compactor for MCP Coding Agents

Empirical Case Study: `ctxfw/cli.py` Core Dependency Graph