Tiger Salesforce MCP Server

A wrapper around our Salesforce database, which contains embedded case summaries. This provides some focused tools to LLMs via the Model Context Protocol.

The raw data is sourced from Salesforce via a Fivetran connection. This populates a schema in a TimescaleDB database. A separate process generates LLM summaries of the support cases, and then embeddings of those summaries. This service searches those summaries.

API

All methods are exposed as MCP tools and REST API endpoints.

Salesforce Case Summary Semantic Search

Searches the Salesforce case summaries for relevant entries based on a semantic embedding of the search prompt.

Tool name : semanticSearchSalesforceCaseSummaries

API endpoint : GET /api/semantic-search/salesforce-case-summaries

Input

(use query parameters for REST API)

{
  "prompt": "Why can't I connect to my database?",
  "limit": 10, // optional, default is 10
}

Output

{
  "results": [
    {
      "case_id": "500Nv000005HMfaIAG",
      "summary": "# Some content ...",
      "distance": 0.40739564321624144,
    },
    // more results...
  ],
}

Development

Cloning and running the server locally.

git clone git@github.com:timescale/tiger-salesforce-mcp-server.git

Building

Run npm i to install dependencies and build the project. Use npm run watch to rebuild on changes.

Create a .env file based on the .env.sample file.

cp .env.sample .env

Testing

The MCP Inspector is very handy.

npm run inspector

Testing in Claude Desktop

Create/edit the file ~/Library/Application Support/Claude/claude_desktop_config.json to add an entry like the following, making sure to use the absolute path to your local tiger-salesforce-mcp-server project, and real database credentials.

{
  "mcpServers": {
    "tiger-salesforce": {
      "command": "node",
      "args": [
        "/absolute/path/to/tiger-salesforce-mcp-server/dist/index.js",
        "stdio"
      ],
      "env": {
        "PGHOST": "x.y.tsdb.cloud.timescale.com",
        "PGDATABASE": "tsdb",
        "PGPORT": "32467",
        "PGUSER": "readonly_mcp_user",
        "PGPASSWORD": "abc123",
        "OPENAI_API_KEY": "sk-svcacct"
      }
    }
  }
}

Deployment

We use a Helm chart to deploy to Kubernetes. See the chart/ directory for details.

The service is accessible to other services in the cluster via the DNS name tiger-salesforce-mcp-server.savannah-system.svc.cluster.local.

Secrets

Run the following to create the necessary sealed secrets. Be sure to fill in the correct values.

kubectl -n savannah-system create secret generic tiger-salesforce-mcp-server-database \
  --dry-run=client \
  --from-literal=user="readonly_mcp_user" \
  --from-literal=password="abc123" \
  --from-literal=database="tsdb" \
  --from-literal=host="x.y.tsdb.cloud.timescale.com" \
  --from-literal=port="34240" \
  -o yaml | kubeseal -o yaml

kubectl -n savannah-system create secret generic tiger-salesforce-mcp-server-openai \
  --dry-run=client \
  --from-literal=apiKey="sk-svcacct-" \
  -o yaml | kubeseal -o yaml

kubectl -n savannah-system create secret generic tiger-salesforce-mcp-server-logfire \
  --dry-run=client \
  --from-literal=token="pylf_v1_us_" \
  -o yaml | kubeseal -o yaml

kubectl -n savannah-system create secret generic tiger-salesforce-mcp-server-tailscale \
  --dry-run=client \
  --from-literal=authkey="tskey-auth-" \
  -o yaml | kubeseal -o yaml

Update ./chart/values/dev.yaml with the output.