The TypeScript framework for secure MCP servers.
MCP Fusion is a TypeScript framework that enforces security at the architectural level of every MCP server. Raw data never reaches the LLM without passing through a typed egress firewall. Tools are physically removed from the agent's namespace when the workflow state forbids them. Every behavioral surface is hashed, locked, and auditable in version control.
The framework ships with a SKILL.md — a machine-readable architectural contract. AI coding agents read the Skill and produce correct, governed servers on the first pass.
MCP Fusion includes a SKILL.md that encodes the entire MVA architecture, security patterns, and governance rules into a format AI coding agents consume directly.
Open your project in Cursor, Claude Code, GitHub Copilot, or Windsurf and describe what you need:
"Build an MCP server for patient records with Prisma. Redact SSN and diagnosis from LLM output. Gate discharge tools until attending physician signs off."
The agent reads the Skill. It produces defineModel() declarations with m.hidden() for sensitive fields, definePresenter() with .redactPII(['*.ssn', '*.diagnosis']) for DLP compliance, FSM state gating via .bindState() for workflow enforcement, and file-based routing under src/tools/. You review the PR.
The Skill is not documentation. It is the security contract. Every server the AI produces inherits the governance stack because the Skill encodes Presenters, state machines, and lockfile generation as mandatory structural patterns.
📄 SKILL.md · llms.txt (complete API reference for LLM consumption)
The Presenter validates every response through a Zod schema compiled from defineModel(). Undeclared fields are stripped in RAM before serialization. PII is redacted via V8-optimized fast-redact compiled functions. Rules travel with data, not in the system prompt. The Late Guillotine pattern applies redaction after UI blocks render — charts and suggestions always see full data, the wire never does.
const PatientPresenter = createPresenter('Patient')
.schema(PatientModel)
.redactPII(['*.ssn', '*.diagnosis'])
.rules((p) => [
p.status === 'critical' ? 'PRIORITY: Patient is critical.' : null,
])
.suggest((p) => p.status === 'admitted'
? [suggest('ward.discharge', 'Begin discharge protocol')]
: []);The Presenter also runs a PromptFirewall — an LLM-as-Judge that evaluates dynamically generated system rules for prompt injection before they reach the agent. Fail-closed by default.
Tools bound to FSM states are physically removed from tools/list when the current state does not match. The LLM cannot call what does not exist in its namespace. Powered by XState v5 with manual fallback when XState is not installed.
const gate = f.fsm({
id: 'discharge', initial: 'admitted',
states: {
admitted: { on: { PHYSICIAN_SIGNOFF: 'approved' } },
approved: { on: { DISCHARGE: 'discharged' } },
discharged: { type: 'final' },
},
});
export default f.mutation('ward.discharge')
.bindState('approved', 'DISCHARGE')
.handle(async (input, ctx) => ctx.db.patients.discharge(input.id));| State | Visible tools |
|---|---|
admitted |
ward.view, ward.update_vitals |
approved |
ward.discharge, ward.view |
discharged |
ward.view |
Serverless-compatible: FsmStateStore persists state to Redis/KV across request boundaries. Each request gets an isolated gate.clone().
Eight introspection modules that make behavioral changes visible and auditable:
| Module | What it does |
|---|---|
| ToolContract | Materializes the complete behavioral surface of each tool |
| BehaviorDigest | SHA-256 hash of the behavioral surface |
| CapabilityLockfile | mcpfusion.lock — git-diffable behavioral snapshot, CI gate via fusion lock --check |
| CryptoAttestation | HMAC-SHA256 runtime verification — fail-fast if behavioral digest drifts |
| ContractDiff | Per-field diff between lockfile versions |
| EntitlementScanner | Static analysis of handler source for I/O capabilities (fs, network, subprocess, eval) with evasion heuristics |
| SemanticProbe | LLM-as-Judge for detecting semantic drift in handler output |
| TokenEconomics | Context window inflation risk profiling |
SandboxEngine executes LLM-provided JavaScript in a sealed V8 isolate. No process, require, fs, or network access. One isolate per engine, fresh empty context per execution. Memory-limited, timeout-enforced, output-capped, abort-signal-compatible.
version: "1.0"
server:
name: "github-tools"
connections:
github:
type: rest
base_url: "https://api.github.com"
auth:
type: bearer
token: "${SECRETS.GITHUB_TOKEN}"
tools:
- name: search_repos
description: "Search GitHub repositories"
instruction: "Use for finding projects by topic or keyword."
rules:
- "Max 10 results per query"
parameters:
query: { type: string, required: true }
execute:
connection: github
method: GET
path: "/search/repositories"
query: { q: "{{query}}", per_page: "10" }
response:
extract: ["items[].{full_name, description, stargazers_count, html_url}"]mcpfusion yaml devexport const InvoiceModel = defineModel('Invoice', m => {
m.casts({
id: m.string(),
amount_cents: m.number('CRITICAL: in CENTS. Divide by 100 for display.'),
status: m.enum('Status', ['paid', 'pending', 'overdue']),
});
m.hidden(['password_hash', 'internal_margin']);
});
export const InvoicePresenter = definePresenter({
name: 'Invoice',
schema: InvoiceModel,
suggestActions: (inv) => inv.status === 'pending'
? [{ tool: 'billing.pay', reason: 'Process payment', args: { id: inv.id } }]
: [],
});
export default f.query('billing.get_invoice')
.describe('Get an invoice by ID')
.withString('id', 'Invoice ID')
.returns(InvoicePresenter)
.handle(async (input, ctx) => ctx.db.invoices.findUnique({ where: { id: input.id } }));State-gated tool discovery. Tools appear and disappear based on the current state.
npx @mcpfusion/core create my-server
cd my-server && npm run devFile-based routing — drop a file, restart, and it's a live MCP tool:
src/tools/
├── billing/
│ ├── get_invoice.ts → billing.get_invoice
│ └── pay.ts → billing.pay
└── users/
└── list.ts → users.list
mcpfusion deploy # Vinkius Edge (V8 Isolate)
vercel deploy # Vercel Functions
wrangler deploy # Cloudflare Workersmcpfusion create my-server # Vanilla
mcpfusion create my-api --vector prisma # Prisma + field-level security
mcpfusion create ops-bridge --vector n8n # n8n workflow bridge
mcpfusion create petstore --vector openapi # OpenAPI → MCP
mcpfusion create my-server --target vercel --yes # Vercel Functions
mcpfusion create my-server --target cloudflare --yes # Cloudflare Workers| Package | Purpose |
|---|---|
@mcpfusion/core |
Framework core — Presenters, Fluent API, middleware, routing, governance |
@mcpfusion/yaml |
Declarative YAML engine |
@mcpfusion/swarm |
Multi-agent orchestration — HMAC-SHA256 delegation, namespace isolation, W3C tracing |
@mcpfusion/a2a |
A2A Protocol Bridge — Agent Cards, task delegation |
@mcpfusion/skills |
Progressive SKILL.md disclosure for agents |
@mcpfusion/testing |
In-memory MVA pipeline testing |
@mcpfusion/inspector |
Real-time TUI dashboard |
| Package | Target |
|---|---|
@mcpfusion/vercel |
Vercel Functions (Edge / Node.js) |
@mcpfusion/cloudflare |
Cloudflare Workers |
| Package | Purpose |
|---|---|
@mcpfusion/openapi-gen |
OpenAPI 3.x / Swagger 2.0 → MCP tools |
@mcpfusion/prisma-gen |
Prisma schema → CRUD tools with field-level security |
@mcpfusion/n8n |
n8n workflows → MCP tools |
@mcpfusion/aws |
AWS Lambda & Step Functions → MCP tools |
| Package | Purpose |
|---|---|
@mcpfusion/oauth |
RFC 8628 Device Flow |
@mcpfusion/jwt |
JWT verification — HS256 / RS256 / ES256 + JWKS |
@mcpfusion/api-key |
API key validation with timing-safe comparison |
Your server runs alongside Salesforce, Stripe, OpenAI, and 4,000+ others. V8 sandbox isolation, DLP, audit trails, and kill switch — all included. No credit card required.
- Sign up at vinkius.com
- Create an App Connector in the dashboard
- Copy your deploy token from the connector settings
- Build your MCP server with MCP Fusion
- Deploy
mcpfusion deployYour MCP server is live.
Every MCP server on vinkius.com is built with MCP Fusion.
Salesforce (12 tools), Slack (8 tools), Stripe, OpenAI, Gmail, WhatsApp Business, Instagram, PayPal, CrowdStrike Falcon, SAP S/4HANA, Workday, DocuSign, Zendesk, Okta, Twilio, Tableau, HubSpot, Shopify, WooCommerce, Airbnb, Tesla Fleet API, NVIDIA AI, Mistral AI, Anthropic, Box, Meta Ads, X Ads, Reddit, Notion, Supabase, Pinecone, Datadog, Sentry — and thousands more.
Four verticals. 36+ subcategories. All governed.
AI Stack — Cognition & RAG, Code Execution, Databases, Observability, DevOps, AI Models, Agent Coordination, Security, Payments & Infra.
Enterprise — CRM, ERP, HR, Legal & Compliance, Customer Support, Marketing, BI & Analytics, Identity & IAM, Communications, E-Commerce, Accounting, Project Management.
Industries — Hospitality, Healthcare, Energy & Commodities, Construction, Real Estate, Agriculture, Wine & Spirits, Education, Logistics, Insurance, Fitness, Travel.
World Data — Economy & Finance, Central Banks, Securities & Markets, Weather & Climate, Demographics, Space & Astronomy, Health & Medicine, Environment, Energy, Food & Nutrition, Government, Trade & Labor.
Every server runs inside V8 isolate sandboxes on AWS. Ed25519 signed audit chains. Sub-40ms cold starts. DLP redaction on every response. Kill switch for instant shutdown. Every tool call logged and auditable.
vinkius.com/discover — Browse the catalog. vinkius.com/developers — Build your own with MCP Fusion.
mcpfusion.vinkius.com · llms.txt · SKILL.md
See CONTRIBUTING.md for development setup and guidelines.
See SECURITY.md for reporting vulnerabilities.