sure/docs/hosting/mcp.md

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 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:

# 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:

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:

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

Or create a Secret and reference it:

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.

Example Requests

Initialize

Handshake to verify protocol version and capabilities:

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:

{
  "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:

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:

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:

{
  "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 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

See the Pipelock documentation and the example configuration in compose.example.pipelock.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 for Pipelock ingress setup

Production Deployment

For a production-ready setup with security scanning:

1. Download the example configuration:

   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:

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

3. Start the stack:

   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 - Configure Sure's chat to use an external agent
• Pipelock Security Proxy - Set up security scanning for MCP traffic
• Model Context Protocol Specification - Official MCP documentation