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  3. 4da Mcp Server
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Health: Not checked yetWe have not completed a health check for this listing yet.Last checked 8/10/2026, 11:43:58 PM

4da Mcp Server

Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository

Dependency intelligence for AI agents. CVE scanning, health checks, upgrade planning.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Install Config Generator

Choose your client
claude_desktop_config.json
{
  "mcpServers": {
    "4da-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "4da-mcp-server"
      ]
    }
  }
}

πŸ’‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

Install Directory Badge Claim listing AlternativesπŸ”’ More in Security

Documentation Overview

4DA

CI License: FSL-1.1 MCP Server Platform

All signal. No feed.


4DA reads the internet for developers β€” privately, locally β€” and gets sharper every day.

It scans your codebase β€” Cargo.toml, package.json, go.mod, Git history β€” and scores every article, advisory, and release from 20+ sources against what you actually build. An item needs 2+ independent signals to survive. Everything else is rejected.

Tested across 9 developer personas: 92% of content is filtered as noise, 98% of actual noise is correctly rejected. Your real rejection rate β€” computed from your own data β€” is shown in the Evidence tab.

It learns from how you engage with what it shows you. Save something β€” topics boost, source reputation rises, your taste embedding sharpens. Dismiss something β€” anti-patterns form, future noise drops. Yesterday's noise becomes tomorrow's signal.

The fastest way to try it

Already using Claude Code, Cursor, or Windsurf? One command:

Terminal
npx @4da/mcp-server

This scans your project, detects your stack, and gives your AI assistant live vulnerability scanning, dependency health, upgrade planning, and ecosystem intelligence. No API keys. No accounts. Works standalone β€” no desktop app required. Full MCP documentation.

4DA Brief tab β€” top picks and live signal stream scored against your stack


How It Works

Scoring

5 independent signal axes. An item must pass 2 or more to surface. Single-axis matches are hard-capped at 28% β€” no matter how strong one signal is, it cannot pass alone.

AxisWhat it measures
ContextSemantic similarity to your active codebase
InterestAlignment with your declared and learned topics
ACEReal-time signals from your Git commits and file edits
DependencyDirect matches against your installed packages
LearnedSave/dismiss feedback boosts or suppresses future scores

What passes the gate goes through 12 quality multipliers: content depth, novelty detection, competing tech penalties, title-body coherence, and intent scoring from recent work. Every constant is calibrated across 9 simulated developer personas with 215 labeled test items.

LLM Verification

After keyword scoring, an LLM layer verifies the top items against your full developer context β€” stack, dependencies, recent commits, anti-technologies, and engagement history. Strict 1-5 rubric:

  • 5 = MUST-READ: Security alert for YOUR dependency, breaking change YOU must act on
  • 3 = WORTH KNOWING: Useful tool that fits YOUR exact stack
  • 1 = NOISE: Mentions your tech but isn't actionable

This is where the gold surfaces β€” articles the keyword pipeline misses because there's no keyword overlap, but the LLM understands the conceptual relevance to your specific project.

You own the compute. Use Ollama for free local inference (fully private), or bring your own Anthropic/OpenAI key. 4DA never pays for your compute, never stores your keys remotely, never makes API calls you didn't configure.

Anti-Gaming

Content creators who learn the scoring algorithm still can't game it:

  • Title-body coherence: titles must deliver on what they promise. Claim "React + Rust + Tauri" but only discuss React? Penalty.
  • Keyword concentration: repeating "Rust" four times in a title hurts your score.
  • Confirmation gate: keyword-stuffing hits one axis. Without matching the user's codebase, installed packages, AND recent work β€” the gate rejects it.
  • Feedback loop: gamed articles get dismissed. Sources that produce dismissed content lose reputation. Gaming becomes self-defeating.

No algorithm can be gamed when the scoring signal comes from your local filesystem. Your Cargo.lock doesn't lie.


Privacy & Trust

4DA is local-first and direct-to-provider. There is no 4DA-operated server, no analytics, and no user account system. Your indexed content, scores, and intelligence live in a SQLite database on your machine.

The only outbound traffic:

CategoryWhereWhy
Source adaptersHN, GitHub, Reddit, arXiv, etc.Fetching public content you configured
LLM providersAnthropic / OpenAI / localhost OllamaOnly if YOU set up BYOK keys
License validationKeygenOnly if you activated a paid license
UpdaterGitHub ReleasesSigned via minisign, once per session
Crash reportsNone4DA sends no crash reports. Export a scrubbed diagnostic bundle locally, on demand.

That's the whole list. There is no 4DA telemetry endpoint because there is no 4DA cloud.

Don't take our word for it:

Network TransparencyEvery outbound connection, with source code references
Trust ArchitectureWhy local-first means you don't need to trust us
Privacy (Plain Language)One-page, no-legalese privacy summary
Security Audit GuideMap of trust-critical code paths for auditors
Build from SourceCompile it yourself and verify the binary

Download

Pre-built binaries β€” no Rust toolchain required.

PlatformDownloadAuto-updates
Windows.exe installerYes
macOS.dmg (Apple Silicon & Intel)Yes
Linux.AppImage / .debYes

Every release publishes SHASUMS256.txt and per-file .sha256 sidecars. Verification instructions.

Windows users: SmartScreen will prompt on first launch (new application, building reputation). Click More info β†’ Run anyway. Full details.

Or install the MCP server for Claude Code / Cursor / Windsurf:

Terminal
npx @4da/mcp-server

Build from Source

bash
git clone https://github.com/4DA-Systems/4DA.git
cd 4DA
pnpm install
pnpm tauri dev   # First build: 5-15 min. Dev server: localhost:4444.

Prerequisites: Rust (1.93.1 via rust-toolchain.toml), Node.js 20, pnpm 9.15. Platform-specific: Windows needs VS Build Tools 2022 with C++ workload. Full build guide.

First-run setup (API keys, context dirs, sources): Getting Started.


System Requirements

4DA runs on modest hardware. Private semantic search is built in β€” no GPU, no API key, and no first-run download required.

Baseline (free)+ Cloud AI (BYOK)+ Local AI (offline)
RAM4 GB4 GB16 GB (8B model); 32 GB for 12–14B
CPUany 64-bitany 64-bit6–8 cores
GPUnot needednot neededoptional (recommended for speed)
Disk~500 MB~500 MB+ 5–9 GB per model
Networkinstall onlyinstall + your AI providerinstall + one model download (or none with Ollama)
  • OS: Windows 10 (1803+) / 11, macOS 10.15+, Ubuntu 22.04+ (WebKitGTK 4.1).
  • One installer (~110 MB). The local embedding model ships inside it β€” no separate download, works fully offline on first run.
  • Baseline = private on-device semantic search. Cloud AI adds AI-written briefings + deeper reranking via your own API key. Local AI runs everything offline (Ollama or a downloaded model).

Architecture

Code
Your Codebase                    External Sources
     |                                |
     v                                v
+-----------+                +--------------+
|    ACE    |                |  20+ Source   |
| Scanner + |                |  Adapters     |
| Git Watch |                |  (background) |
+-----+-----+               +------+-------+
      |                            |
      v                            v
+------------------------------------------+
|         5-Axis Scoring Engine            |
|                                          |
|  context --+                             |
|  interest --+- confirmation gate (2+/5)  |
|  ace -------+                            |
|  dependency-+  x quality x novelty       |
|  learned ---+  x domain  x intent        |
+------------------+-----------------------+
                   |
                   v
          +-----------------+
          |  What survived  |
          +-----------------+
LayerTechnology
App ShellTauri 2.0 (Rust backend + WebView)
FrontendReact 19 + TypeScript + Tailwind CSS v4
DatabaseSQLite 3.45+ with sqlite-vec (vector search)
ScoringCustom pipeline β†’ build-time Rust codegen
EmbeddingsOpenAI text-embedding-3-small / Ollama
LLMAnthropic Claude / OpenAI / Ollama (BYOK)

Pricing

Free β€” $0 forever. No credit card. No account. No expiration.

  • All 20+ sources, full 5-axis scoring engine, AI daily briefings (BYOK), natural language search (BYOK), behavior learning, MCP server (14 tools), CLI

Signal β€” $12/month or $99/year (14-day free trial).

  • Everything in Free, plus: Signal tab intelligence (Key Signals + analytics), Score Autopsy (5-axis breakdown), Developer DNA profiling, signal chain analysis, knowledge gap detection, semantic shift tracking, attention analytics, standing queries, project health radar

Free is not a demo. It's the full scoring engine, all sources, behavior learning, and MCP integration.

The STREETS Playbook β€” 7 modules on turning developer skills into independent income β€” is free on the open web. No download, no email.


Features

Intelligence
  • 5-axis scoring with multi-signal confirmation gate (92% rejection, 98% noise accuracy across 9 test personas)
  • Domain profile: graduated tech identity (primary stack β†’ dependencies β†’ detected β†’ interests)
  • Content DNA: classifies content type (security advisory, release, tutorial, hiring, etc.)
  • Novelty detection: demotes introductory content, boosts new releases and security advisories
  • Role-aware scoring: security engineers see security content prominently; experience level adjusts tutorial/depth balance
  • Intent scoring: recent Git/file activity influences what surfaces
  • Knowledge gap detection: finds blind spots in your dependency understanding
  • Anti-gaming: title-body coherence, keyword concentration, adversarial resistance built into the pipeline
Sources β€” 20+ adapters, all running locally
  • Hacker News, GitHub, Reddit, YouTube, arXiv, Stack Overflow
  • Lobsters, DEV.to, Product Hunt, Twitter/X, Bluesky, Hugging Face
  • Papers with Code, crates.io, npm, PyPI, Go modules
  • CVE/OSV vulnerability databases, custom RSS feeds
Analysis
  • Signal chains: tracks evolving stories across sources
  • Semantic shift detection: notices when topics you follow are changing
  • Reverse mentions: finds where your projects are discussed
  • Project health radar: dependency freshness + security monitoring
  • Attention dashboard: where you spend time vs. where you should
Decision Intelligence
  • Record and query architectural decisions across sessions
  • Tech radar: adoption signals from decisions + content trends
  • Decision enforcement: AI agents check alignment before suggesting changes
Agent Autonomy
  • Cross-session, cross-agent persistent memory
  • Session briefs: tailored startup context for any AI tool
  • Delegation scoring: should the agent proceed or ask you?
  • Developer DNA: exportable tech identity profile (markdown, SVG, or shareable card)
MCP Integration β€” 14 tools for dependency security, intelligence, decisions, and agent memory

Plug your intelligence system directly into Claude Code, Cursor, Windsurf, VS Code (Copilot), or any MCP-compatible tool.

Terminal
npx @4da/mcp-server

9 tools work standalone with zero setup (vulnerability scanning, dependency health, upgrade planning, ecosystem news, pre-task briefings, decision memory, agent memory). 5 more activate with the desktop app (scored content feed, actionable signals, knowledge gaps, feedback learning, developer DNA). Every tool reliably returns useful data. Full tool reference.

CLI

Reads from the same database as the desktop app. No extra setup.

bash
4da briefing               # Latest AI briefing
4da signals                # All classified signals
4da signals --critical     # Critical/high priority only
4da gaps                   # Knowledge gaps in your dependencies
4da health                 # Project dependency health
4da status                 # Database stats

Screenshots

Brief tab
Brief β€” today's top picks and live signal stream scored against your stack

Preemption Radar
Preemption β€” forward-looking intelligence: CVEs, breaking changes, dependency risks

Blind Spot Index
Blind Spots β€” coverage gaps and high-relevance items you never saw

Signal tab
Signal β€” the items that earn their place, confirmed through 2+ independent axes


Development

4DA is built by a solo engineer with AI-assisted development (Claude Code), with external audits from gpt 5.4 and 5.5. The test suite (3,400+ tests across Rust and TypeScript) and CI pipeline verify correctness on every commit. The scoring algorithm is hand-designed and benchmarked against 9 developer personas with labeled test data.

bash
pnpm tauri dev              # Dev server (localhost:4444)
cargo test                  # Rust tests (from src-tauri/)
pnpm test                   # Frontend tests
pnpm validate:all           # Full validation (lint + types + tests + build)

Benchmarks

The scoring claims in this README are tested, not asserted. The benchmark suite runs the full PASIFA pipeline against 9 simulated developer personas (Rust systems, Python ML, fullstack TypeScript, DevOps/SRE, mobile, bootstrap/first-run, power user, stack switcher, niche specialist) with labeled test items scored as relevant or noise.

bash
cd src-tauri
cargo test scoring::benchmark -- --nocapture    # Full benchmark with output
cargo test scoring::simulation -- --nocapture   # Persona simulation suite

Source: src-tauri/src/scoring/benchmark.rs (1,335 lines, 27 tests) and src-tauri/src/scoring/simulation/ (persona definitions, domain embeddings, enrichment data).


License

FSL-1.1-Apache-2.0 β€” source available. Free to use, inspect, and modify for any purpose except building a competing product. Every release converts to Apache 2.0 three years after publication β€” after that, no restrictions at all.


4DA β€” 4 Dimensional Autonomy

All signal. No feed.


"4DA" and the 4DA logo are trademarks of 4DA Systems Pty Ltd (ACN 696 078 841). The FSL-1.1-Apache-2.0 license does not grant rights to use these trademarks.

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Frequently Asked Questions about 4da Mcp Server

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "4da-mcp-server": { "command": "npx", "args": ["-y", "4da-mcp-server"] } }

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Technical Specs & Signals

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RuntimeNode.js
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

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