DataPilot 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_sqlB | Execute a SQL query on Snowflake and return results |
| list_databasesB | List all databases available to the user |
| list_schemasC | List all schemas in a database |
| list_tablesB | List all tables in a database/schema |
| describe_tableC | Get detailed information about a table's columns |
| get_table_sampleC | Get a sample of data from a table |
| list_warehousesB | List all warehouses available to the user |
| get_warehouse_statusB | Get current warehouse, database, and schema status |
| natural_language_to_sqlC | Convert natural language question to SQL query using AI |
| analyze_query_resultsC | Execute a query and analyze its results using AI |
| suggest_query_optimizationsB | Get AI-powered suggestions for optimizing a SQL query |
| explain_queryA | Explain what a SQL query does in plain English |
| generate_table_insightsC | Generate AI-powered insights about a table's data |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| sql_analysis_prompt | Generate a prompt for analyzing SQL query results |
| data_exploration_prompt | Generate a prompt for exploring a data table |
| sql_optimization_prompt | Generate a prompt for SQL query optimization |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| get_databases_resource | Resource to get list of databases |
TDQS
Scored across 13 tools
Every tool has a clearly distinct purpose with no ambiguity. For example, 'execute_sql' runs queries while 'explain_query' describes them, and 'list_databases' enumerates databases whereas 'describe_table' provides column details. The tools cover different aspects of the data workflow without overlap.
All tool names follow a consistent verb_noun pattern using snake_case, such as 'list_databases', 'execute_sql', and 'generate_table_insights'. This uniformity makes the toolset predictable and easy to navigate, with no deviations in naming conventions.
With 13 tools, the count is well-scoped for a Snowflake data management server. Each tool serves a specific function in querying, listing, analyzing, or optimizing data, and none appear redundant, fitting the domain appropriately.
The toolset provides complete coverage for data exploration and SQL workflows, including listing resources (databases, schemas, tables), executing and explaining queries, generating insights, and optimizing performance. There are no obvious gaps, enabling agents to handle end-to-end tasks without dead ends.