Redshift MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| execute_sqlC | Execute a SQL Query on the Redshift cluster |
| analyze_tableC | Analyze table to collect statistics information |
| get_execution_planC | Get actual execution plan with runtime statistics for a SQL query |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| All Schemas in Databases | List all schemas in Redshift database |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: analyze_table focuses on table statistics, execute_sql runs queries, and get_execution_plan provides query performance insights. There is no overlap in functionality, making it easy for an agent to select the right tool.
All tool names follow a consistent verb_noun pattern (analyze_table, execute_sql, get_execution_plan) with clear, descriptive verbs. The naming is uniform and predictable throughout the set.
With only 3 tools, the set feels thin for a database server like Redshift, which typically involves more operations such as data manipulation, schema management, or monitoring. While the tools cover core query execution and analysis, the scope seems limited.
There are significant gaps in the tool surface for a Redshift server. Missing are essential operations like creating/dropping tables, inserting/updating data, listing databases or tables, and user/permission management. This incomplete coverage will likely cause agent failures in broader database workflows.