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Context Firewall

by Alepha188838884version 0.1.1i★ –iRuns on your computer
Registry nameio.github.Alepha188838884/context-firewall

Install

Overview

Description from the maker (original English)

Local MCP proxy: collapses N servers' tools into 4 meta-tools and compresses large tool outputs.

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: Cybersecurity

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": {
    "context-firewall": {
      "command": "npx",
      "args": [
        "-y",
        "context-firewall",
        "--config",
        "your-value"
      ]
    }
  }
}

Run this in your terminal

claude mcp add context-firewall -- npx -y context-firewall --config your-value

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

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

{
  "mcpServers": {
    "context-firewall": {
      "command": "npx",
      "args": [
        "-y",
        "context-firewall",
        "--config",
        "your-value"
      ]
    }
  }
}

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 needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "context-firewall": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "context-firewall",
        "--config",
        "your-value"
      ]
    }
  }
}

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 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Alepha188838884, 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 Cybersecurityi

  • More stars than 0% of the other 995 servers in this category
  • Published to the registry more recently than 31% of the other 995 servers in this category
  • Basic checks equal to or better than 100% of the other 995 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 Context Firewall 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 (0.1.1) 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: WatchGuard MCP, ipdata MCP by usefulapi, MaxMind minFraud MCP by usefulapi

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

WatchGuard MCP

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WatchGuard Cloud Firebox, Endpoint Security, ThreatSync, NDR and AuthPoint APIs for every customer

Runs on your computerUpdated today
Install

Look up location, network, company and threat data for IP addresses and ASNs in ipdata.

Runs in the cloudUpdated today
Install

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.

Context Firewall

中文文档

Shrink large MCP tool outputs by 60–95% before they reach your model's context window — and collapse 50+ tool definitions into 4. Real HTML/JSON, measured — see benchmarks. Works with any MCP client, any model. Anything still over your configured token budget after compression is hard-truncated to that budget, with the full original always retrievable via read_more.

*The session report printed on shutdown — this one from a real 3-call session (two large file reads, one echo). Every number measured, none simulated.*

Context Firewall is a local MCP proxy that sits between your AI agent (Claude Code, Claude Desktop, Cursor, Cline, ...) and every downstream MCP server you've configured. Large tool outputs (raw HTML, base64 blobs, giant JSON) are compressed before they ever reach the model's context window, and the client sees exactly 4 tools no matter how many the downstream servers actually have.

Measured results

MetricResult
Output compression70–94% on real HTML pages, ~97% on large structured JSON (smart stages alone, before budget truncation) — e.g. a live Wikipedia page via the fetch tool: 232,391 → 6,907 chars (97.0%, measured); a GitHub issues JSON payload via the jsonSummary stage: 186,810 → 3,480 chars (98.1%, measured)
Tool collapse122 → 4 exposed meta-tools (5 real downstream servers incl. official GitHub github-mcp-server, 85 tools)
Tool-definition savings~28,600 tokens *(estimated, chars ÷ 3.5)* — 102,158 raw definition chars vs. 2,146 exposed

All figures measured against real downstream MCP servers, not synthetic data — full methodology and tables in docs/BENCHMARKS.md.

What it does

  • Progressive tool disclosure — instead of loading every downstream tool's full schema at startup, the client sees 4 meta-tools (list_tool_categories, search_tools, invoke_tool, read_more) and only pays the token cost of a tool's full schema when it actually searches for it.
  • Output compression pipeline — large tool results are run through base64 stripping, main-content extraction + HTML→Markdown conversion (page chrome — nav, site headers/footers — is stripped when the page has a recognizable content region, so your token budget goes to actual content instead of navigation links), JSON structure-aware summarization, and finally character-budget truncation, in that order, before being returned.
  • Full output retrievable via `read_more` — nothing is silently thrown away. Every compressed output is stored in full (in memory, opaque handle) and can be paged back with read_more(handle, offset, length).
  • Session savings report — on shutdown, prints a shareable terminal card (and optional Markdown file) showing tool-definition and output-token savings for the session, plus a breakdown of which tools saved the most.

Quickstart

npx context-firewall --config context-firewall.json

Minimal context-firewall.json (the downstreams block mirrors the mcpServers format you already know):

{

"downstreams": { "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"] }, "github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"], "env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}" } } } }

(${GITHUB_TOKEN} above is just this example's environment variable *name* — pick whatever's already set in your shell; it gets expanded into GITHUB_PERSONAL_ACCESS_TOKEN, the env var name the downstream server itself actually reads.)

Which GitHub server? Two options, different tool counts:

  • `@modelcontextprotocol/server-github` (used above) — the original npm package, 26 tools, one npx -y line, zero extra setup. Archived/no longer maintained upstream, but still functional.
  • [`github/github-mcp-server`](https://github.com/github/github-mcp-server) — the actively-maintained official server, 44 tools (default toolset) to 85 (GITHUB_TOOLSETS=all). Ships as a Go binary or Docker image, not an npm package:
  "github": {

"command": "docker", "args": ["run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN", "ghcr.io/github/github-mcp-server"], "env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}" } }

or, running a locally-built/downloaded binary directly: "command": "/path/to/github-mcp-server", "args": ["stdio"] (same env block; add GITHUB_TOOLSETS to scope which of the 85 tools are exposed).

Per-tool allow/deny policy. Add allowTools/denyTools (array of exact names or * globs) to any downstream entry to restrict which of its tools can be invoked:

"github": {

"command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"], "denyTools": ["delete_*"] }

Deny always wins over allow. When allowTools is set, only matching tools are permitted; everything else on that server is blocked. An empty allowTools: [] is treated the same as omitting it (allow everything), not "deny everything". Blocked tools are hidden from search_tools results, and invoke_tool rejects them before dispatching to the downstream server. Tool counts in list_tool_categories and in the meta-tool descriptions are unfiltered totals — the policy is only enforced at search_tools/invoke_tool time.

Client setup

Set Context Firewall as your only MCP server — move every downstream server you currently configure directly (filesystem, github, everything, ...) into context-firewall.json's downstreams block instead. Your agent then sees 4 tools instead of the sum of every downstream server's tool count; pointing the client at Context Firewall *alongside* your existing servers doesn't give you the tool-collapse or compression benefit.

Each client below takes the same server entry:

{

"mcpServers": { "context-firewall": { "command": "npx", "args": ["-y", "context-firewall", "--config", "/absolute/path/to/context-firewall.json"] } } }

Claude Code

Project-scoped .mcp.json in your repo root (shown above), or via the CLI:

claude mcp add --transport stdio context-firewall -- npx -y context-firewall --config /absolute/path/to/context-firewall.json
Claude Desktop

claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json; Windows: %APPDATA%\Claude\claude_desktop_config.json) — same mcpServers block as above.

Cursor

.cursor/mcp.json (project-scoped) or ~/.cursor/mcp.json (global) — same mcpServers block as above.

Cline

cline_mcp_settings.json (VS Code extension storage; macOS: ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json) — same mcpServers block as above.

Compatibility status
ClientStatus
Claude Codetested in real agent sessions — autonomous list → search → invoke → read_more workflow verified end-to-end
Claude Desktopprotocol-verified*
Cursorconfig format documented, community testing welcome
Clineconfig format documented, community testing welcome

\* Verified via MCP protocol integration tests (236 automated tests, including full stdio protocol round-trips against real downstream servers, run in CI on every push). Real-client reports welcome.

Configuration

downstreams

Each entry is either a stdio server (same shape as mcpServers) or a Streamable HTTP server:

{

"downstreams": { "local-tool": { "command": "npx", "args": ["-y", "some-mcp-server"], "env": { "TOKEN": "${TOKEN}" } }, "remote-tool": { "url": "https://mcp.example.com/mcp", "transport": "streamable-http" } } }

${VAR_NAME} in any string value is expanded from the environment; a missing variable fails config load with a readable error.

compression

Policy resolution order is default < perServer < perTool (later overrides earlier, field by field):

{

"compression": { "default": { "maxOutputTokens": 2000, "htmlToMarkdown": true, "stripBase64": true, "jsonSummary": true, "llmSummary": false, "bypass": false }, "perServer": { "github": { "maxOutputTokens": 4000 } }, "perTool": { "filesystem/read_file": { "maxOutputTokens": 8000 } } } }

FieldTypeDefaultMeaning
maxOutputTokensnumber2000Soft budget (chars ≈ tokens × 3.5) a compressed output is truncated to as a last resort.
htmlToMarkdownbooleantrueConvert detected HTML to Markdown, extracting the main content region (nav/header/footer chrome stripped) when one is recognizable.
stripBase64booleantrueReplace base64 blobs (data URIs and bare blocks) with a read_more handle.
jsonSummarybooleantrueCollapse homogeneous JSON arrays and trim long string fields, keeping valid JSON.
llmSummarybooleanfalseSemantically summarize over-budget outputs with an LLM of your choice. Requires the top-level llm block - see LLM summarization (opt-in).
bypassbooleanfalseSkip the whole pipeline for this server/tool - output passes through untouched.
report
{

"report": { "enabled": true, "markdownPath": "./context-firewall-report.md" } }

FieldTypeDefaultMeaning
enabledbooleantruePrint the session report to stderr on shutdown.
markdownPathstring*(none)*If set, also write the report as a Markdown file at this path.
callToolTimeoutMs

Top-level (not nested under compression). Per-invoke_tool timeout in milliseconds passed to the downstream MCP SDK client; a downstream that hangs without responding causes invoke_tool to return an isError result once this elapses, instead of blocking. Defaults to the SDK's own default (60,000ms) when unset.

{ "callToolTimeoutMs": 30000 }

How it works

The client calls list_tool_categories() to see what's connected and what it's roughly capable of, search_tools(query) to pull the full input schema for candidate tools, invoke_tool(server, tool, args) to actually run one (compressed on the way back), and read_more(handle, offset, length) to page through anything that got compressed. Compression, when it runs, always applies in the same order: strip base64 → main-content extraction + HTML to Markdown → JSON structure summary → truncate to budget (if the opt-in LLM summarization stage below is enabled, it runs right before truncation). Content extraction is conservative by design: it only strips chrome when a semantic content region ( / ) is recognizable or the removal clearly isn't the page's actual content, and falls back to whole

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