Consentry MCP server
Commit 23 Okt 2026Berfungsi sebagai firewall persetujuan bagi AI agent dengan menerapkan minimisasi data di tingkat field, prompt persetujuan real-time, dan karantina prompt-injection.
Juga dikenal sebagai vnmoorthy/consentry
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Apa itu Consentry MCP?
Consentry adalah MCP server. Berfungsi sebagai firewall persetujuan bagi AI agent dengan menerapkan minimisasi data di tingkat field, prompt persetujuan real-time, dan karantina prompt-injection.
Ringkasan dibuat dari README proyek
Tentang Consentry MCP server
Consentry melindungi informasi pribadi dari AI agent dengan bertindak sebagai firewall persetujuan antara model dan tool eksternal. Sistem ini berada di antara agent dan API yang terhubung, MCP server, atau perusahaan lain untuk menegakkan minimisasi data dan keamanan. Sistem akan memeriksa field yang keluar untuk memastikan detail sensitif seperti nomor kartu kredit atau lokasi presisi diganti dengan versi yang diminimalkan, seperti kartu virtual sekali pakai atau koordinat tingkat lingkungan saja.
Developer dapat menerapkan ini sebagai MCP server atau HTTP gateway. Sistem ini bekerja dengan client seperti Claude Desktop, Claude Code, dan Cursor. Server dapat dijalankan secara lokal di browser tanpa backend atau sebagai layanan berbasis Node yang memerlukan Anthropic API key untuk integrasi Claude. Consentry menggunakan per-field policy engine untuk memutuskan apakah akan mengizinkan, meminimalkan, menanyakan, atau menolak akses data berdasarkan tingkat kepercayaan penerima. Untuk menjaga integritas, sistem ini mengelola ledger SHA-256 hash-chained yang membuat tanda terima privasi yang tahan manipulasi untuk setiap interaksi agent.
Apa yang bisa Anda lakukan dengannya
- 1Minimalkan pembagian data sensitifAgent dapat memberikan lokasi tingkat lingkungan alih-alih koordinat GPS yang presisi saat memesan sebuah layanan.
- 2Cegah serangan prompt injectionFirewall ini memindai respons tool yang masuk untuk mengarantina konten berbahaya atau upaya eksfiltrasi tersembunyi.
- 3Terapkan persetujuan pengguna secara real-timeSistem akan menghentikan sementara agent untuk membiarkan pengguna memilih antara membagikan data presisi atau versi yang telah diminimalkan.
- 4Audit riwayat paparan dataPengguna dapat meninjau matriks mendetail tentang penerima mana saja yang memiliki akses ke tipe data spesifik milik mereka.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Konfigurasi
CONSENTRY_URL
| Nama | Deskripsi | Tipe |
|---|---|---|
| CONSENTRY_URL | — |
Pengecekan otomatis
Berjalan 15 Nov 2026Laporan lengkap →Belum diverifikasi. Sebuah server memerlukan skor 80, tidak ada temuan serius, dan data yang cukup untuk kami periksa. Cara kerja checks · Yang perlu diperbaiki untuk menambah poin
- Source repository terhubung dan dapat dijangkauvnmoorthy/consentry is public and reachablegithub.com/vnmoorthy/consentry ↗Lolos
- Nama paket mencantumkan repositori ini sebagai sumbernyaTidak diperiksa
- Provenance build (atestasi Sigstore)Tidak diperiksa
- Lisensi open source (OSI)MITspdx.org/licenses/MIT.html ↗Lolos
- Ada commit atau release dalam satu tahun terakhirLast commit or release 2026-10-23github.com/vnmoorthy/consentry ↗Lolos
- OpenSSF ScorecardTidak diperiksa
- Nama package tidak mirip dengan package populerTidak diperiksa
- Tidak ada advisory kritis pada paket atau dependensinyaTidak diperiksa
- Tidak ada instruksi tersembunyi untuk model0 tool descriptions and the README read; no override phrases, credential paths or hidden charactersLolos
- Tools yang bersifat destruktif telah diberi labelTidak diperiksa
- Endpoint remote merespons handshake MCPTidak diperiksa
README
Lihat di GitHubConsentry is a consent firewall that sits between an AI agent and everything it talks to: tools, APIs, MCP servers, other companies, and even the LLM itself. It inspects every outbound field, minimizes what isn't needed, asks you when it matters, quarantines prompt injections coming back, and writes a tamper-evident receipt of every decision.
Live demo · Watch the demo video · Architecture · 10-minute demo script · Claude mode · Pitch storyboard
Table of contents
- Why this exists
- What it does
- See it in action
- How it works
- Quickstart
- Use it with Claude Desktop, Claude Code, Cursor (MCP)
- Policies in plain English
- Red team: attack your own agent
- Public data APIs
- Security model & honest limitations
- Project structure
- Roadmap
💡 Why this exists
Personal agents are getting real power: they book tables, refill prescriptions, call ride services, query health databases. To do that they need your data: your address, your phone, your card, your medications.
Today that data is all-or-nothing. Once it is in the agent's context window it can go to any tool, any third party and the model provider. A single poisoned web page or tool response can tell the agent to send it somewhere else, and nobody can later prove what was shared, with whom, or why.
Consent banners don't fix this. Model alignment doesn't either, because a model that usually refuses is not a control. The boundary has to be enforced outside the model. That's Consentry.
The design principle: don't trust the model, enforce at the boundary. Even a fully compromised agent (we simulate one) can't move your SSN, your real card number or your mental-health data, because those bytes never get past the firewall.
✨ What it does
| Capability | What it means for you | |
|---|---|---|
| 🧬 | Field-level PII detection | Vault fingerprints, vault tokens, regex with checksums (Luhn, SSN rules), a health lexicon, and geo-proximity to your saved places. It catches your data even when it isn't labeled. |
| ⚖️ | Per-field policy engine | Each field is judged on data type × recipient trust tier × purpose: allow, minimize, ask or deny. Highest priority wins, then the most specific rule, then the most restrictive. |
| ✂️ | Minimizers | Exact GPS → ~10 km grid · address → neighborhood or nearest intersection · email/phone → relay aliases · card → single-use, merchant-locked virtual card · birthday → age range · allergy → "peanut-free meal required" · drug → generic name only. |
| ✋ | Real consent | The agent pauses. You see the exact vs. minimized value side by side, the purpose, the recipient's retention policy, and the rule that triggered. Choose minimized · once · 30 days · deny. Grants expire and can be revoked. |
| 🤖 | The LLM is a recipient too | Claude only ever sees {{vault:tokens}}. Prompts and tool results are tokenized before they reach the model; tokens are resolved at the network edge only if policy allows. |
| ☣️ | Ingress quarantine | Tool responses are untrusted. A noisy-OR scanner catches prompt injection, hidden HTML comments, zero-width text, exfil image URLs and templated data slots, then strips them, taints the run and locks sensitive data. |
| 🚫 | Default-deny egress | Unverified domains never receive a request. If a policy withholds a field a tool requires, the call never leaves. |
| 🧾 | Hash-chained ledger + receipts | Every decision is appended to a SHA-256 hash chain over canonical JSON, with masked values and a digest of the exact payload sent. Every agent run produces a privacy receipt. Edit one past entry and verification fails from that entry onward. |
| 🔐 | Encrypted vault | AES-256-GCM at rest via WebCrypto, with the same code in Node (key file, chmod 600) and the browser (non-extractable key in IndexedDB). |
| 👣 | Your data footprint | A who-knows-what matrix (every recipient × every data type: knows exactly · approximately · protected) computed only from the ledger. |
| 🧹 | Make them forget you | One click drafts a CCPA/GDPR deletion request listing exactly what a partner received, citing ledger entries as evidence, and revokes the relay aliases and single-use cards you issued them. The agent can do it too: "Ask TableTap to delete my data". |
| 🎬 | Guided demo tour | 12 chapters (6–9 minutes) with talking points, one-click actions and spotlighted UI, plus a compact mode for presenting. |
| 🔌 | Works with any agent | Ships as an MCP server and an HTTP gateway. It also runs fully local-first in the browser: the live demo has no backend at all. |
🎬 See it in action
🧠 How it works
Every tool call, from Claude, the on-device Autopilot, or any MCP/HTTP client, goes through one choke point: ConsentryRuntime.callTool().
sequenceDiagram
autonumber
participant U as You
participant A as Agent (Claude / Autopilot / MCP client)
participant F as Consentry firewall
participant V as Vault (AES-256-GCM)
participant T as Tool / API / Partner
participant L as Ledger (SHA-256 chain)
U->>A: "Book a vegetarian dinner for 2 tonight"
Note over A: Sees only {{vault:phone}}, {{vault:card}}…
A->>F: book_table(name={{vault:full_name}}, phone={{vault:phone}}, card={{vault:card}}, notes={{vault:allergy_peanut}})
F->>V: resolve tokens
F->>F: detect → classify → evaluate policy per field
F-->>U: ✋ TableTap wants "Food allergy" (exact vs. "Peanut-free meal required")
U->>F: Share minimized
F->>L: append egress entry (masked fields + payload digest)
F->>T: name ✓ · phone → relay alias · card → single-use virtual card · notes → minimized
T-->>F: response (untrusted)
F->>F: ingress scan: injection? exfil URL? smuggled tokens?
F->>A: sanitized, tokenized result
A-->>U: answer + 🧾 privacy receipt
Decision pipeline (per field)
detect ─▶ classify (16 data types) ─▶ evaluate rules (priority ▸ specificity ▸ restrictiveness)
│ │
│ allow ◀──────────┼──────────▶ deny (withhold; block call if field is required)
│ minimize ◀─────────┴──────────▶ ask (pause agent → you decide → optional grant w/ expiry)
▼
rewrite payload ─▶ default-deny check ─▶ ledger.append() ─▶ send ─▶ ingress quarantine ─▶ agent
Read the full design in docs/ARCHITECTURE.md.
🚀 Quickstart
Requirements: Node ≥ 22.5 and pnpm (Corepack works).
git clone https://github.com/vnmoorthy/consentry.git && cd consentry
pnpm install
pnpm start # builds the dashboard and starts the gateway on http://localhost:8787
Open http://localhost:8787 and click a scenario chip. No API key is needed: the Autopilot agent runs on-device and calls real public APIs through the firewall.
Use Claude as the agent brain (optional):
cp .env.example .env # set ANTHROPIC_API_KEY=… (defaults to claude-opus-5-5)
pnpm start
Or paste a key in Settings; it is stored encrypted with the vault. Claude still only ever sees tokens. See docs/CLAUDE_MODE.md for the Claude demo segment and pnpm tsx scripts/claude-check.ts, which proves no raw vault value reaches the model. If Claude is unreachable, runs fall back to Autopilot automatically.
| Command | What it does |
|---|---|
pnpm start |
Build the web app and run the gateway + dashboard on :8787 |
pnpm dev |
Gateway on :8787 + Vite dev server on :5173 with hot reload |
pnpm test |
Unit tests for detection, policy, transforms, firewall, injection scanner and ledger |
pnpm tsx scripts/smoke.ts |
End-to-end run of every scenario + red-team attack against the live public APIs |
pnpm tsx scripts/claude-check.ts |
Claude mode end to end: intercepts every Anthropic request and fails if any raw vault value is sent |
sh scripts/set-key.sh |
Save your Anthropic key to .env with hidden input, and verify it |
pnpm mcp |
Start the MCP bridge (stdio) |
pnpm build:pages |
Static, backend-free build (the whole firewall runs in the browser) |
🔌 Use it with any agent (MCP + HTTP)
Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"consentry": {
"command": "npx",
"args": ["tsx", "/absolute/path/to/consentry/apps/mcp/src/index.ts"],
"env": { "CONSENTRY_URL": "http://localhost:8787" }
}
}
}
Claude Code: claude mcp add consentry -- npx tsx ./apps/mcp/src/index.ts
Your MCP client now gets 19 tools (weather, air quality, NWS alerts, quakes, NHTSA, openFDA, FEMA, Wikipedia, bookings, rides, refills…). Every call is firewalled, consent prompts appear in the Consentry dashboard, and everything lands in your ledger.
Any agent framework, over plain HTTP:
curl -s http://localhost:8787/api/gateway/call \
-H 'content-type: application/json' \
-d '{"tool":"air_quality","agent":"my-bot","args":{"location":"{{vault:home_location}}"}}'
# → data + "Home GPS minimized → 37.8,-122.4 (≈10 km)"
| Method | Route | Purpose |
|---|---|---|
GET |
/api/state |
Snapshot: masked vault, policies, recipients, tools, stats, pending consents |
GET |
/api/events |
Server-sent events: every step, decision, consent and ledger append |
POST |
/api/chat |
Start an agent run { message } → { runId } |
POST |
/api/consent/:id |
Answer a consent prompt { decision: allow_once | allow_always | minimize | deny } |
GET |
/api/gateway/tools |
Tool catalog with JSON schemas |
POST |
/api/gateway/call |
Firewalled tool call { tool, args, agent } |
POST |
/api/policies/compile |
Plain English → structured rule |
POST |
/api/policies/simulate |
Explain what would happen for item × recipient × purpose |
POST |
/api/ledger/verify |
Re-hash the chain and report the first broken entry |
GET |
/api/ledger/export |
Export the full ledger (JSON) |
POST |
/api/redteam/:id |
Run a red-team scenario |
📝 Policies in plain English
Type a sentence and Consentry compiles it into a structured, explainable rule:
| You write | Consentry compiles |
|---|---|
| Never share my location with commercial apps | location.precise → partner : deny |
| Let TableTap see my phone number for reservations for 24 hours | contact.phone → tabletap (reservation) : allow · expires in 24h |
| Only share my approximate location with ride services | location.precise → partner (transport) : minimize(coarse-location) |
| Never let the AI see my medications | health.medication → llm : deny |
Privacy-first defaults ship out of the box, including hard lines that no grant or click can override: government IDs, credentials, mental-health data, marketing use, and unverified destinations.
☠️ Red team: attack your own agent
Every scenario runs through the real runtime, firewall, tools and ledger. Run them in the UI or with pnpm tsx scripts/smoke.ts.
| Attack | What happens | Result |
|---|---|---|
| Poisoned restaurant review | A hidden HTML comment tells the agent to send your SSN and card to telemetry-cdn.io. We also simulate an agent that obeys. |
✅ Ingress quarantine (score 1.0) → run taint → default-deny egress blocks the exfil call → SSN/card withheld from the booking |
| Zero-click image exfiltration | A partner response embeds  |
✅ Exfil URL stripped, smuggled token neutralized, renderer never loads remote images |
| Secrets pasted into the prompt | The user pastes a card number and SSN into chat | ✅ The model receives My card is {{vault:card}} and my SSN is [Government ID withheld] |
| Over-collecting partner | An insurance API asks for 8 fields for a quote | ✅ 2 exact (with consent), 4 minimized, 2 withheld, and the quote still comes back |
| Rewrite the audit log | An insider edits a past entry to say nothing was shared | ✅ Hash mismatch detected at that exact entry |
🌎 Public data APIs (all live)
No keys and no accounts. Every call goes through the firewall, which is why account-less public APIs only ever get a coarsened location.
Public data is real but imperfect, so Consentry says what each source can't tell you: every answer that used public data ends with a Data limits note, Claude is handed the same caveats in its tool results, and the Connections page lists them per source.
| API | Used for | Recipient tier | Known limits |
|---|---|---|---|
| Open-Meteo Forecast | 7-day forecast, current conditions | Open data | Model forecast on a ~11 km grid; less reliable beyond 3 days |
| Open-Meteo Air Quality | US AQI, PM2.5, ozone, 3-day outlook | Open data | Modeled, not a nearby sensor reading; local smoke can differ |
| Open-Meteo Geocoding | Place → coordinates | Open data | Best-known match; ambiguous names can land in the wrong spot |
| National Weather Service | Active alerts | Public sector | US only; an alert exists only once a forecast office issues it |
| USGS Earthquakes | Quakes near you | Public sector | Magnitudes are preliminary and often revised |
| NHTSA vPIC | VIN decoding | Public sector | Built from manufacturer filings; trim details can be missing |
| NHTSA Recalls | Vehicle safety recalls | Public sector | By make/model/year: can't tell whether this car was already repaired |
| openFDA Drug Enforcement | Drug recalls | Public sector | Reports lag by weeks; matches drug name, not your lot |
| openFDA Drug Label | Warnings & interactions | Public sector | Manufacturer text, not medical advice |
| openFDA Food Enforcement | Allergen recalls | Public sector | Reports lag by weeks; check the lot on the package |
| OpenFEMA | Disaster declarations | Public sector | Federally declared disasters only, usually days after the event |
| Wikipedia REST | Encyclopedia lookups | Open data | Crowd-edited and can be out of date |
Commercial partners (BiteFinder, TableTap, RideNow, MediMart, QuickQuote) are simulated locally and labeled as such in the UI, so the demo can show hostile behavior safely and deterministically.
🛡️ Security model & honest limitations
What Consentry guarantees: if a value is in your vault, or matches a sensitive pattern, it cannot leave the runtime in a form your policy doesn't allow. That covers tokens, raw values, reformatted values, coordinates near your saved places, and values pasted into prompts.
What it doesn't (yet):
- Semantic paraphrase. An agent that describes your condition in new words ("the user has a lung thing") isn't caught by fingerprints. The health lexicon helps, and an LLM-based classifier is on the roadmap.
- Encoded exfiltration to allowed recipients. Default-deny stops unknown hosts, but a trusted recipient could receive data in a channel the detector doesn't parse.
- The re-seal is local. Re-sealing a tampered ledger changes the head hash, which is detectable by anyone holding the old head. Production would anchor the head externally (transparency log / notarization).
- The demo persona is fictional (555-01xx phone,
example.comemail, Stripe test card, the famously void SSN 078-05-1120).
See SECURITY.md to report issues.
🗂️ Project structure
consentry/
├── packages/core/ # Isomorphic engine (Node + browser): zero runtime deps besides the Anthropic SDK
│ ├── src/detect.ts # vault fingerprints, tokens, regex+checksums, lexicon, geo-proximity
│ ├── src/policy.ts # rule evaluation + plain-English compiler + grants
│ ├── src/transforms.ts # minimizers (coarse location, aliases, virtual cards, age ranges…)
│ ├── src/firewall.ts # egress inspection, consent, rewrite, ingress quarantine, LLM tokenization
│ ├── src/injection.ts # prompt-injection / exfil scanner (noisy-OR over 12 signals)
│ ├── src/ledger.ts # SHA-256 hash chain over canonical JSON
│ ├── src/planner.ts # Autopilot: on-device agent, no LLM
│ ├── src/llm.ts # Claude tool-use loop (only sees tokens)
│ ├── src/runtime.ts # the single choke point + events, receipts, stats
│ ├── src/redteam.ts # 5 attack scenarios
│ ├── src/footprint.ts # who-knows-what matrix + CCPA/GDPR deletion letters
│ └── src/tools/ # 12 live public-data tools + 5 simulated partners + 1 on-device tool
├── apps/server/ # Hono gateway: REST + SSE + SQLite ledger + AES-GCM vault
├── apps/web/ # React 19 + Vite + Tailwind v4 + Framer Motion dashboard
├── apps/mcp/ # MCP bridge (stdio) → gateway
├── scripts/ # smoke test, screenshot capture, asset rendering
├── deck/ # 10-slide animated pitch deck (.pptx)
└── docs/ # architecture, demo script, storyboard, idea log
🧭 Roadmap
- LLM-assisted semantic classifier for paraphrased sensitive data
- Signed policy bundles + external ledger anchoring (Sigstore / transparency log)
- Browser extension: the same firewall in front of ChatGPT, Claude.ai and other web agents
- Data-subject requests: one click to ask a partner to delete what it got, with ledger evidence
- Real delivery of deletion requests (email/API) and tracking of statutory deadlines
- Policy packs for GDPR, CCPA/CPRA and HIPAA "minimum necessary"
- Multi-agent: per-agent identities and budgets ("this agent may share 3 contact fields per day")
🤝 Contributing
PRs are welcome. Run pnpm test && pnpm tsx scripts/smoke.ts before opening one. See CONTRIBUTING.md.
📄 License
MIT. Built for the National Renaissance Hackathon at super{set} (tracks: Hacking with Public Data APIs · Personal Agents & Data Privacy).
Pertanyaan tentang Consentry MCP
Apa fungsi Consentry bagi AI agent?
Consentry berada di antara AI agent dengan tool atau API miliknya untuk memeriksa field yang keluar, meminimalkan data sensitif, meminta persetujuan pengguna, dan mengarantina prompt injection.
Bagaimana cara ia melindungi data saya dari LLM itu sendiri?
Sistem ini melakukan tokenisasi pada prompt dan hasil tool sehingga model hanya melihat vault token, bukan informasi sensitif yang mentah.
Apakah saya bisa menggunakannya dengan Claude Desktop atau Cursor?
Ya, ini tersedia sebagai MCP server yang dapat bekerja dengan agent seperti Claude Desktop, Claude Code, dan Cursor.
Apakah memerlukan API key untuk menjalankannya?
Tidak memerlukan API key untuk local Autopilot agent, meskipun Anda dapat secara opsional memberikan Anthropic API key untuk menggunakan Claude sebagai otaknya.