# MCP Server for External AI Assistants

Sure includes a Model Context Protocol (MCP) server endpoint that allows external AI assistants like Claude Desktop, GPT agents, or custom AI clients to query your financial data.

## What is MCP?

[Model Context Protocol](https://modelcontextprotocol.io/) is a JSON-RPC 2.0 protocol that enables AI assistants to access structured data and tools from external applications. Instead of copying and pasting financial data into a chat window, your AI assistant can directly query Sure's data through a secure API.

This is useful when:
- You want to use an external AI assistant (Claude, GPT, custom agents) to analyze your Sure financial data
- You prefer to keep your LLM provider separate from Sure
- You're building custom AI agents that need access to financial tools

## Prerequisites

To enable the MCP endpoint, you need to set two environment variables:

| Variable | Description | Example |
|----------|-------------|---------|
| `MCP_API_TOKEN` | Bearer token for authentication | `your-secret-token-here` |
| `MCP_USER_EMAIL` | Email of the Sure user whose data the assistant can access | `user@example.com` |

Both variables are **required**. The endpoint will not activate if either is missing.

### Generating a secure token

Generate a random token for `MCP_API_TOKEN`:

```bash
# macOS/Linux
openssl rand -base64 32

# Or use any secure password generator
```

### Choosing the user

The `MCP_USER_EMAIL` must match an existing Sure user's email address. The AI assistant will have access to all financial data for that user's family.

> [!CAUTION]
> The AI assistant will have **read access to all financial data** for the specified user. Only set this for users you trust with your AI provider.

## Configuration

### Docker Compose

Add the environment variables to your `compose.yml`:

```yaml
x-rails-env: &rails_env
  MCP_API_TOKEN: your-secret-token-here
  MCP_USER_EMAIL: user@example.com
```

Both `web` and `worker` services inherit this configuration.

### Kubernetes (Helm)

Add the variables to your `values.yaml` or set them via Secrets:

```yaml
env:
  MCP_API_TOKEN: your-secret-token-here
  MCP_USER_EMAIL: user@example.com
```

Or create a Secret and reference it:

```yaml
envFrom:
  - secretRef:
      name: sure-mcp-credentials
```

## Protocol Details

The MCP endpoint is available at:

```
POST /mcp
```

### Authentication

All requests must include the `MCP_API_TOKEN` as a Bearer token:

```
Authorization: Bearer <MCP_API_TOKEN>
```

### Supported Methods

Sure implements the following JSON-RPC 2.0 methods:

| Method | Description |
|--------|-------------|
| `initialize` | Protocol handshake, returns server info and capabilities |
| `tools/list` | Lists available financial tools with schemas |
| `tools/call` | Executes a tool with provided arguments |

### Available Tools

The MCP endpoint exposes these financial tools:

| Tool | Description |
|------|-------------|
| `get_transactions` | Retrieve transaction history with filtering |
| `get_accounts` | Get account information and balances |
| `get_holdings` | Query investment holdings |
| `get_balance_sheet` | Current financial position (assets, liabilities, net worth) |
| `get_income_statement` | Income and expenses over a period |
| `import_bank_statement` | Import bank statement data |
| `search_family_files` | Search uploaded documents in the vault |

These are the same tools used by Sure's builtin AI assistant.

### Preview Tools

These additional tools appear only when the MCP user has opted into preview
features (Settings → Preferences). Until then they are absent from `tools/list`,
and calling one by name returns an "Unknown tool" error. The Statement Vault
tools additionally require the user to be an admin or member, matching the
permissions enforced in the web UI.

| Tool | Description |
|------|-------------|
| `upload_account_statement` | Store a statement document (PDF/CSV/XLSX) in the Statement Vault; deduplicates by SHA-256 |
| `list_account_statements` | List vault documents with their SHA-256, period, linked account and review status |
| `get_account_statement` | One statement's details, a short-lived download URL, and its reconciliation checks against the ledger — present only once someone has entered the statement's opening/closing balances in the web UI, since nothing extracts them from the document |
| `get_statement_coverage` | Month-by-month statement coverage for an account: covered, missing, mismatched, ambiguous |
| `record_valuation` | Record an account's value on a date, with a required source citation |

They exist for agents that maintain a document-backed record of a family's
wealth over time. See
[Patrimonial history with an external agent harness](../llm-guides/patrimonial-agent-harness.md).

## Example Requests

### Initialize

Handshake to verify protocol version and capabilities:

```bash
curl -X POST https://your-sure-instance/mcp \
  -H "Authorization: Bearer your-secret-token" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "initialize"
  }'
```

Response:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "protocolVersion": "2025-03-26",
    "capabilities": {
      "tools": {}
    },
    "serverInfo": {
      "name": "sure",
      "version": "1.0"
    }
  }
}
```

### List Tools

Get available tools with their schemas:

```bash
curl -X POST https://your-sure-instance/mcp \
  -H "Authorization: Bearer your-secret-token" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 2,
    "method": "tools/list"
  }'
```

Response includes tool names, descriptions, and JSON schemas for parameters.

### Call a Tool

Execute a tool to get transactions:

```bash
curl -X POST https://your-sure-instance/mcp \
  -H "Authorization: Bearer your-secret-token" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 3,
    "method": "tools/call",
    "params": {
      "name": "get_transactions",
      "arguments": {
        "start_date": "2024-01-01",
        "end_date": "2024-01-31"
      }
    }
  }'
```

Response:

```json
{
  "jsonrpc": "2.0",
  "id": 3,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "[{\"id\":\"...\",\"amount\":-45.99,\"date\":\"2024-01-15\",\"name\":\"Coffee Shop\"}]"
      }
    ]
  }
}
```

## Security Considerations

### Transient Session Isolation

The MCP controller creates a **transient session** for each request. This prevents session state leaks that could expose other users' data if the Sure instance is using impersonation features.

Each MCP request:
1. Authenticates the token
2. Loads the user specified in `MCP_USER_EMAIL`
3. Creates a temporary session scoped to that user
4. Executes the tool call
5. Discards the session

This ensures the AI assistant can only access data for the intended user.

### Pipelock Security Scanning

For production deployments, we recommend using [Pipelock](https://github.com/luckyPipewrench/pipelock) to scan MCP traffic for security threats.

Pipelock provides:
- **DLP scanning**: Detects secrets being exfiltrated through tool calls
- **Prompt injection detection**: Identifies attempts to manipulate the AI
- **Tool poisoning detection**: Prevents malicious tool call sequences
- **Policy enforcement**: Block or warn on suspicious patterns
- **Signed receipts**: Produces verifiable evidence for mediated MCP decisions when the flight recorder is configured with storage and a signing key

See the [Pipelock documentation](pipelock.md) and the example configuration in `compose.example.ai.yml` for setup instructions.

### Network Security

The `/mcp` endpoint is exposed on the same port as the web UI (default 3000). For hardened deployments:

**Docker Compose:**
- The MCP endpoint is protected by the `MCP_API_TOKEN` but is reachable on port 3000
- For additional security, use Pipelock's MCP reverse proxy (port 8889) which adds scanning
- See `compose.example.ai.yml` for a Pipelock configuration

**Kubernetes:**
- Use NetworkPolicies to restrict access to the MCP endpoint
- Route external agents through Pipelock's MCP reverse proxy
- See the [Helm chart documentation](../../charts/sure/README.md) for Pipelock ingress setup

## Production Deployment

For a production-ready setup with security scanning:

1. **Download the example configuration:**

   ```bash
   curl -o compose.ai.yml https://raw.githubusercontent.com/we-promise/sure/main/compose.example.ai.yml
   curl -o pipelock.example.yaml https://raw.githubusercontent.com/we-promise/sure/main/pipelock.example.yaml
   ```

2. **Set your MCP credentials in `.env`:**

   ```bash
   MCP_API_TOKEN=your-secret-token
   MCP_USER_EMAIL=user@example.com
   ```

3. **Start the stack:**

   ```bash
   docker compose -f compose.ai.yml up -d
   ```

4. **Connect your AI assistant to the Pipelock MCP proxy:**

   ```
   http://your-server:8889
   ```

The Pipelock proxy (port 8889) scans all MCP traffic before forwarding to Sure's `/mcp` endpoint.

## Connecting AI Assistants

### Claude Desktop

Configure Claude Desktop to use Sure's MCP server:

1. Open Claude Desktop settings
2. Add a new MCP server
3. Set the endpoint to `http://your-server:8889` (if using Pipelock) or `http://your-server:3000/mcp`
4. Add the authorization header: `Authorization: Bearer your-secret-token`

### Custom Agents

Any AI agent that supports JSON-RPC 2.0 can connect to the MCP endpoint. The agent should:

1. Send a POST request to `/mcp`
2. Include the `Authorization: Bearer <token>` header
3. Use the JSON-RPC 2.0 format for requests
4. Handle the protocol methods: `initialize`, `tools/list`, `tools/call`

## Troubleshooting

### "MCP endpoint not configured" error

**Symptom:** Requests return HTTP 503 with "MCP endpoint not configured"

**Fix:** Ensure both `MCP_API_TOKEN` and `MCP_USER_EMAIL` are set as environment variables and restart Sure.

### "unauthorized" error

**Symptom:** Requests return HTTP 401 with "unauthorized"

**Fix:** Verify the `Authorization` header contains the correct token: `Bearer <MCP_API_TOKEN>`

### "MCP user not configured" error

**Symptom:** Requests return HTTP 503 with "MCP user not configured"

**Fix:** The `MCP_USER_EMAIL` does not match an existing user. Check that:
- The email is correct
- The user exists in the database
- There are no typos or extra spaces

### Pipelock connection refused

**Symptom:** AI assistant cannot connect to Pipelock's MCP proxy (port 8889)

**Fix:**
1. Verify Pipelock is running: `docker compose ps pipelock`
2. Check Pipelock health: `docker compose exec pipelock /pipelock healthcheck --addr 127.0.0.1:8888`
3. Verify the port is exposed in your `compose.yml`

## See Also

- [External AI Assistant Configuration](ai.md#external-ai-assistant) - Configure Sure's chat to use an external agent
- [Pipelock Security Proxy](pipelock.md) - Set up security scanning for MCP traffic
- [Model Context Protocol Specification](https://modelcontextprotocol.io/) - Official MCP documentation