Snowflake ACTION
Execute SQL Query In Session
Execute a Snowflake query inside a session started by the Start SQL Session action, so it shares that session's state — temporary tables,
USE context, and session parameters — with the other queries in the same session. Pass the serialized session from Start SQL Session to the Session prop. Note: to preserve the session this query runs on a direct connection and therefore does NOT egress from the shared static IP (use_pd_sql_proxy); if your Snowflake network policy allowlists only the static IP, use Execute SQL Query instead. See our docs to learn more about working with SQL in Pipedream.- Action
- Writes data
- API key
- SDK
- MCP
IMPLEMENTATION
Call this tool
Connect a user's Snowflake account once, then configure and run Execute SQL Query In Session from your backend or agent.
import { PipedreamClient } from "@pipedream/sdk"
const pd = new PipedreamClient({
projectId: process.env.PIPEDREAM_PROJECT_ID!,
clientId: process.env.PIPEDREAM_CLIENT_ID!,
clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
projectEnvironment: "production",
})
const result = await pd.actions.run({
id: "snowflake-execute-sql-query-in-session",
externalUserId: "{external_user_id}", // any stable ID for this user in your system
configuredProps: {
snowflake: { authProvisionId: "apn_xxxxxxx" },
session: "Session",
sql: "SQL Query",
},
})
console.log(result)curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
-H "Content-Type: application/json" \
-H "X-PD-Environment: production" \
-H "Authorization: Bearer {access_token}" \
-d '{
"external_user_id": "{external_user_id}",
"id": "snowflake-execute-sql-query-in-session",
"configured_props": {
"snowflake": { "authProvisionId": "apn_xxxxxxx" },
"session": "Session",
"sql": "SQL Query"
}
}'// accessToken: mint a short-lived token with the Connect SDK — see the MCP guide
const transport = new StreamableHTTPClientTransport(
new URL("https://remote.mcp.pipedream.net/v3"),
{
requestInit: {
headers: {
Authorization: `Bearer ${accessToken}`,
"x-pd-project-id": "{project_id}",
"x-pd-environment": "production",
"x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
"x-pd-app-slug": "snowflake",
},
},
},
)
const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)
const { tools } = await mcp.listTools()
// listTools() hands your model this tool's input schema, so it can
// fill the arguments itself:
const result = await mcp.callTool({
name: "snowflake-execute-sql-query-in-session",
arguments: {
session: "Session",
sql: "SQL Query",
},
})SCHEMA
Inputs
Pipedream supplies the connected account. Your application provides the operation-specific values below. Dynamic inputs are resolved against that user's account.
| Property | Type | Description |
|---|---|---|
session Session | string | A serialized session created by the Start SQL Session action, e.g. {{steps.start_sql_session.$return_value.session}}. When provided, this action runs in that session, preserving session state such as temporary tables, USE context, and session parameters. Sessions expire server-side after ~4 hours or your account's idle timeout. Required |
sql SQL Query | sql | Required |
REFERENCE
Tool details
Behavior hints are published with the component in the Pipedream registry and surface as MCP tool annotations, so an agent can reason about a tool before it calls it.
- Registry key
- snowflake-execute-sql-query-in-session
- Version
- 0.0.2
- App
- Snowflake
- Authentication
- API key
- Read-only
- No
- Destructive
- No
- Open world
- Yes
- Source
- View on GitHub ↗