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wren-connection-info

Name: Wren Connection Info
Author: Canner

// Reference guide for Wren Engine connection info — explains required fields for all 18 supported data sources (PostgreSQL, MySQL, BigQuery, Snowflake, ClickHouse, Trino, DuckDB, Databricks, Spark, Athena, Redshift, Oracle, SQL Server, Apache Doris, S3, GCS, MinIO, local files). Covers sensitive field handling, Docker host hints, and BigQuery credential encoding. Use when the user asks how to configure a data source connection or what fields to fill in.

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stars:660

forks:200

updated:7 de abril de 2026, 06:01

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SKILL.md

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name	wren-connection-info
description	Reference guide for Wren Engine connection info — explains required fields for all 18 supported data sources (PostgreSQL, MySQL, BigQuery, Snowflake, ClickHouse, Trino, DuckDB, Databricks, Spark, Athena, Redshift, Oracle, SQL Server, Apache Doris, S3, GCS, MinIO, local files). Covers sensitive field handling, Docker host hints, and BigQuery credential encoding. Use when the user asks how to configure a data source connection or what fields to fill in.
license	Apache-2.0
metadata	{"author":"wren-engine","version":"1.5"}

Wren Connection Info Reference

Connection info can only be configured through the MCP server Web UI at http://localhost:9001. Do not attempt to set it programmatically. Always direct the user to the Web UI.

Supported data sources

Value	Database	Fields reference
`POSTGRES`	PostgreSQL	databases.md
`MYSQL`	MySQL / MariaDB	databases.md
`MSSQL`	SQL Server	databases.md
`CLICKHOUSE`	ClickHouse	databases.md
`ORACLE`	Oracle	databases.md
`DORIS`	Apache Doris	databases.md
`REDSHIFT`	Amazon Redshift	databases.md
`TRINO`	Trino	databases.md
`BIGQUERY`	Google BigQuery	databases.md
`SNOWFLAKE`	Snowflake	databases.md
`DUCKDB`	DuckDB	databases.md
`ATHENA`	AWS Athena	databases.md
`DATABRICKS`	Databricks	databases.md
`SPARK`	Apache Spark	databases.md
`S3_FILE`	Amazon S3	file-sources.md
`GCS_FILE`	Google Cloud Storage	file-sources.md
`MINIO_FILE`	MinIO	file-sources.md
`LOCAL_FILE`	Local files	file-sources.md

Read the linked reference file for the user's data source to get required fields, default ports, and setup notes.

Common patterns

Most database connectors need: host, port, user, password, database.

Exceptions:

BigQuery — uses project_id, dataset_id, credentials (base64-encoded). See databases.md for encoding instructions.
Snowflake — uses account instead of host, plus schema.
Trino — needs catalog and schema instead of database.
Databricks — uses serverHostname, httpPath, accessToken (or service principal with clientId, clientSecret).
Spark — only host and port (Spark Connect protocol, no auth fields).
File sources — use url, format, plus bucket/credentials. See file-sources.md.

Docker host hint

If the database runs on the host machine and Wren Engine runs inside Docker, localhost cannot reach the host. Use host.docker.internal instead:

Original	Inside Docker
`localhost`	`host.docker.internal`
`127.0.0.1`	`host.docker.internal`
Cloud/remote hostname	No change needed

Sensitive fields

Never log, display, or pass sensitive values through the AI agent unnecessarily.

Connector	Sensitive fields
Postgres / MySQL / MSSQL / ClickHouse / Oracle / Doris / Redshift	`password`
BigQuery	`credentials`
Snowflake	`password`
Athena	`aws_access_key_id`, `aws_secret_access_key`
Databricks (token)	`accessToken`
Databricks (service principal)	`clientId`, `clientSecret`
S3 / MinIO	`access_key`, `secret_key`
GCS	`key_id`, `secret_key`, `credentials`
Trino / Spark / Local files	(none)

related-skills.json

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wren-onboarding.md

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Onboard a user to Wren Engine end-to-end. Walks through environment checks, project scaffolding, connection configuration via .env, and first query. Use when: user wants to install Wren Engine, set up a new data source connection, or bootstrap a new project from scratch. Triggers: '/wren-onboarding', 'install wren', 'set up wren engine', 'wren onboarding', 'connect new database to wren'.

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wren-generate-mdl.md

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Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.

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wren-usage.md

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Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.

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Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.

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wren-generate-mdl.md

from "Canner/wren-engine"

Generate a Wren MDL manifest from a database using ibis-server metadata endpoints. Use when a user wants to create or set up a new Wren MDL, scaffold a manifest from an existing database, or onboard a new data source without installing any database drivers locally.

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wren-http-api.md

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Interact with Wren Engine MCP server via plain HTTP JSON-RPC requests — no MCP client SDK required. Covers session initialization, tool discovery, and calling all 20+ Wren tools (query, deploy, metadata, health check) using standard HTTP POST with JSON-RPC 2.0 payloads. Use when the client cannot or prefers not to use the MCP protocol directly (e.g. OpenClaw, custom HTTP clients, shell scripts, or any environment without an MCP SDK).

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package.json

"author": "Canner"

"repository": "Canner/wren-engine"

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name	wren-connection-info
description	Reference guide for Wren Engine connection info — explains required fields for all 18 supported data sources (PostgreSQL, MySQL, BigQuery, Snowflake, ClickHouse, Trino, DuckDB, Databricks, Spark, Athena, Redshift, Oracle, SQL Server, Apache Doris, S3, GCS, MinIO, local files). Covers sensitive field handling, Docker host hints, and BigQuery credential encoding. Use when the user asks how to configure a data source connection or what fields to fill in.
license	Apache-2.0
metadata	{"author":"wren-engine","version":"1.5"}

Wren Connection Info Reference

Connection info can only be configured through the MCP server Web UI at http://localhost:9001. Do not attempt to set it programmatically. Always direct the user to the Web UI.

Supported data sources

Value	Database	Fields reference
`POSTGRES`	PostgreSQL	databases.md
`MYSQL`	MySQL / MariaDB	databases.md
`MSSQL`	SQL Server	databases.md
`CLICKHOUSE`	ClickHouse	databases.md
`ORACLE`	Oracle	databases.md
`DORIS`	Apache Doris	databases.md
`REDSHIFT`	Amazon Redshift	databases.md
`TRINO`	Trino	databases.md
`BIGQUERY`	Google BigQuery	databases.md
`SNOWFLAKE`	Snowflake	databases.md
`DUCKDB`	DuckDB	databases.md
`ATHENA`	AWS Athena	databases.md
`DATABRICKS`	Databricks	databases.md
`SPARK`	Apache Spark	databases.md
`S3_FILE`	Amazon S3	file-sources.md
`GCS_FILE`	Google Cloud Storage	file-sources.md
`MINIO_FILE`	MinIO	file-sources.md
`LOCAL_FILE`	Local files	file-sources.md

Read the linked reference file for the user's data source to get required fields, default ports, and setup notes.

Common patterns

Most database connectors need: host, port, user, password, database.

Exceptions:

BigQuery — uses project_id, dataset_id, credentials (base64-encoded). See databases.md for encoding instructions.
Snowflake — uses account instead of host, plus schema.
Trino — needs catalog and schema instead of database.
Databricks — uses serverHostname, httpPath, accessToken (or service principal with clientId, clientSecret).
Spark — only host and port (Spark Connect protocol, no auth fields).
File sources — use url, format, plus bucket/credentials. See file-sources.md.

Docker host hint

If the database runs on the host machine and Wren Engine runs inside Docker, localhost cannot reach the host. Use host.docker.internal instead:

Original	Inside Docker
`localhost`	`host.docker.internal`
`127.0.0.1`	`host.docker.internal`
Cloud/remote hostname	No change needed

Sensitive fields

Never log, display, or pass sensitive values through the AI agent unnecessarily.

Connector	Sensitive fields
Postgres / MySQL / MSSQL / ClickHouse / Oracle / Doris / Redshift	`password`
BigQuery	`credentials`
Snowflake	`password`
Athena	`aws_access_key_id`, `aws_secret_access_key`
Databricks (token)	`accessToken`
Databricks (service principal)	`clientId`, `clientSecret`
S3 / MinIO	`access_key`, `secret_key`
GCS	`key_id`, `secret_key`, `credentials`
Trino / Spark / Local files	(none)

wren-connection-info

Wren Connection Info Reference

Supported data sources

Common patterns

Docker host hint

Sensitive fields

Más de este repositorio

Más de este repositorio

Wren Connection Info Reference

Supported data sources

Common patterns

Docker host hint

Sensitive fields