Provides comprehensive database interaction capabilities including SQL query execution, schema exploration, table management, and data analysis tools for Snowflake data warehouses.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Snowflake MCP Server show me the top 10 customers by total sales this month"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server implementation that provides database interaction with Snowflake. This server enables running SQL queries via tools and exposes data insights and schema context as resources.
Related MCP server: Snowflake MCP Server
memo://insights
A continuously updated memo aggregating discovered data insights.
Updated automatically when new insights are appended via the append_insight tool.
context://table/{table_name}
(If prefetch enabled) Per-table schema summaries, including columns and comments, exposed as individual resources.
The server exposes the following tools:
read_query
Execute SELECT queries to read data from the database.
Input:
query (string): The SELECT SQL query to execute
Returns: Query results as array of objects
write_query (enabled only with --allow-write)
Execute INSERT, UPDATE, or DELETE queries.
Input:
query (string): The SQL modification query
Returns: Number of affected rows or confirmation
create_table (enabled only with --allow-write)
Create new tables in the database.
Input:
query (string): CREATE TABLE SQL statement
Returns: Confirmation of table creation
list_databases
List all databases in the Snowflake instance.
Returns: Array of database names
list_schemas
List all schemas within a specific database.
Input:
database (string): Name of the database
Returns: Array of schema names
list_tables
List all tables within a specific database and schema.
Input:
database (string): Name of the database
schema (string): Name of the schema
Returns: Array of table metadata
describe_table
View column information for a specific table.
Input:
table_name (string): Fully qualified table name (database.schema.table)
Returns: Array of column definitions with names, types, nullability, defaults, and comments
append_insight
Add new data insights to the memo resource.
Input:
insight (string): Data insight discovered from analysis
Returns: Confirmation of insight addition
Effect: Triggers update of memo://insights resource
To install Snowflake Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp_snowflake_server --client claude"mcpServers": {
"snowflake_pip": {
"command": "uvx",
"args": [
"--python=3.12", // Optional: specify Python version <=3.12
"mcp_snowflake_server",
"--account", "your_account",
"--warehouse", "your_warehouse",
"--user", "your_user",
"--password", "your_password",
"--role", "your_role",
"--database", "your_database",
"--schema", "your_schema"
// Optionally: "--allow_write"
// Optionally: "--log_dir", "/absolute/path/to/logs"
// Optionally: "--log_level", "DEBUG"/"INFO"/"WARNING"/"ERROR"/"CRITICAL"
// Optionally: "--exclude_tools", "{tool_name}", ["{other_tool_name}"]
]
}
}Install Claude AI Desktop App
Install uv:
curl -LsSf https://astral.sh/uv/install.sh | shCreate a .env file with your Snowflake credentials:
SNOWFLAKE_USER="xxx@your_email.com"
SNOWFLAKE_ACCOUNT="xxx"
SNOWFLAKE_ROLE="xxx"
SNOWFLAKE_DATABASE="xxx"
SNOWFLAKE_SCHEMA="xxx"
SNOWFLAKE_WAREHOUSE="xxx"
SNOWFLAKE_PASSWORD="xxx"
# Alternatively, use external browser authentication:
# SNOWFLAKE_AUTHENTICATOR="externalbrowser"[Optional] Modify runtime_config.json to set exclusion patterns for databases, schemas, or tables.
Test locally:
uv --directory /absolute/path/to/mcp_snowflake_server run mcp_snowflake_serverAdd the server to your claude_desktop_config.json:
"mcpServers": {
"snowflake_local": {
"command": "/absolute/path/to/uv",
"args": [
"--python=3.12", // Optional
"--directory", "/absolute/path/to/mcp_snowflake_server",
"run", "mcp_snowflake_server"
// Optionally: "--allow_write"
// Optionally: "--log_dir", "/absolute/path/to/logs"
// Optionally: "--log_level", "DEBUG"/"INFO"/"WARNING"/"ERROR"/"CRITICAL"
// Optionally: "--exclude_tools", "{tool_name}", ["{other_tool_name}"]
]
}
}By default, write operations are disabled. Enable them explicitly with --allow-write.
The server supports filtering out specific databases, schemas, or tables via exclusion patterns.
The server exposes additional per-table context resources if prefetching is enabled.
The append_insight tool updates the memo://insights resource dynamically.
MIT
Add a data insight to the memo
| Name | Required | Description | Default |
|---|---|---|---|
| insight | Yes | Data insight discovered from analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Add' implies a write operation but doesn't clarify permissions needed, whether the memo must exist, if insights are editable, or what happens on success/failure. This is inadequate for a mutation tool without annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly for tool selection.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's mutation nature (implied by 'Add'), lack of annotations, and no output schema, the description is insufficient. It doesn't address behavioral aspects like side effects, error handling, or result format, leaving critical gaps for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'insight' parameter documented as 'Data insight discovered from analysis'. The description adds no additional parameter details beyond this, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add') and the target resource ('data insight to the memo'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'read_query' or 'describe_table' which might also involve data insights, leaving room for ambiguity in tool selection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't specify prerequisites (e.g., needing an existing memo), exclusions, or how it relates to siblings like 'read_query' for data analysis, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the schema information for a specific table
| Name | Required | Description | Default |
|---|---|---|---|
| table_name | Yes | Fully qualified table name in the format 'database.schema.table' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't cover important aspects like whether it's a read-only operation, potential error conditions (e.g., if the table doesn't exist), or the format of the returned schema information. This leaves gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without any wasted words. It's front-loaded with the essential information, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that retrieves schema information. It doesn't explain what the output looks like (e.g., column names, data types, constraints) or handle edge cases, which is crucial for an AI agent to use this tool effectively in a database context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'table_name' fully documented in the input schema as a required string in 'database.schema.table' format. The description doesn't add any additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating with extra details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('schema information for a specific table'), making the purpose immediately understandable. However, it doesn't distinguish this tool from potential sibling tools like 'list_tables' or 'read_query', which might also provide table-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'list_tables' (which might list tables without schema details) or 'read_query' (which might execute queries). There's no mention of prerequisites, such as needing to know the table name beforehand from other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch video media for specific violation events. Returns simple list with DEVICE_EVENT_UUID and Region.
| Name | Required | Description | Default |
|---|---|---|---|
| violation_ids | Yes | List of violation IDs to get video media for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the return format ('simple list with DEVICE_EVENT_UUID and Region'), which adds some behavioral context. However, it lacks details on permissions, rate limits, error handling, or data freshness, which are critical for a fetch operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that directly state the purpose and return format. It's front-loaded and wastes no words, though it could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides basic purpose and return format but lacks details on behavioral traits, error cases, or usage context. It's minimally adequate for a simple fetch tool but has clear gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the 'violation_ids' parameter fully. The description doesn't add any meaning beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Fetch video media for specific violation events.' It specifies the verb ('fetch'), resource ('video media'), and target ('violation events'). However, it doesn't distinguish from siblings like 'append_insight' or 'read_query' which serve different functions, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions. It's a basic statement of function with no usage instructions, leaving the agent to infer applicability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available databases in Snowflake
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions listing databases but doesn't specify whether this is a read-only operation, if it requires permissions, what the output format is, or any rate limits. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any fluff or redundant information. It's front-loaded and appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficient for a complete understanding. It doesn't explain what 'list' entails (e.g., format, pagination, or metadata included), which is critical for an agent to use the tool effectively in context with its siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, which is efficient and aligns with the schema's completeness, earning a high score for not adding unnecessary information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all available databases in Snowflake'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_schemas' or 'list_tables' beyond the resource type, which keeps it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'list_schemas' or 'list_tables', nor does it mention any prerequisites or exclusions. It simply states what the tool does without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all schemas in a database
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | Database name to list schemas from |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but does not reveal any behavioral traits such as read-only status, potential side effects, error handling, or output format. This leaves significant gaps for an agent to understand how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that lists data. It does not explain what the output contains (e.g., schema names, metadata) or any behavioral aspects like pagination or errors, leaving the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting the single required parameter 'database'. The description adds no additional semantic meaning beyond what the schema provides, such as parameter constraints or examples, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('list') and resource ('all schemas in a database'), making the purpose unambiguous. However, it does not differentiate from sibling tools like 'list_databases' or 'list_tables' beyond the resource name, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'list_databases' or 'list_tables'. It lacks context about prerequisites, exclusions, or typical use cases, offering only a basic statement of function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all tables in a specific database and schema
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | Database name | |
| schema | Yes | Schema name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists tables but doesn't describe behavioral traits such as whether this is a read-only operation, potential rate limits, authentication requirements, or what the output format looks like (e.g., pagination, error handling). This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, with every part of the sentence contributing essential information about what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool with two required parameters. It doesn't address behavioral aspects like safety, performance, or output format, which are critical for an AI agent to use the tool correctly. The description alone is insufficient to compensate for the missing structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters ('database' and 'schema') fully documented in the input schema. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't explain parameter constraints or relationships). According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List all tables') and the target resource ('in a specific database and schema'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'list_databases' or 'list_schemas' which operate on different resource types, so it doesn't fully differentiate from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_databases' or 'list_schemas' that might be used in related contexts, nor does it specify prerequisites or exclusions for usage. The agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute a SELECT query.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | SELECT SQL query to execute |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose permissions needed, rate limits, whether it's read-only (implied by SELECT but not explicit), output format, or error handling. This is inadequate for a tool with mutation potential in SQL execution.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It lacks context on behavioral traits, usage scenarios, and output expectations, making it insufficient for safe and effective tool invocation in a complex environment with siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the 'query' parameter as a SELECT SQL query. The description adds no additional meaning beyond this, such as syntax examples or constraints. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Execute a SELECT query' clearly states the action (execute) and resource (SELECT query), but it's vague about scope and doesn't distinguish from siblings like 'describe_table' or 'list_tables' which might also involve querying. It specifies SELECT but doesn't clarify if it's for any table or specific databases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. It doesn't mention prerequisites (e.g., database selection), exclusions (e.g., non-SELECT queries), or compare to siblings like 'list_tables' for metadata queries. The description implies usage for SELECT queries but lacks context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose with no overlap: append_insight handles data insights, describe_table provides schema details, fetch_violation_videos retrieves video media, list_databases/schemas/tables enumerate database objects, and read_query executes queries. The tools are well-separated by function and target resources.
All tools follow a consistent verb_noun naming pattern (e.g., append_insight, describe_table, fetch_violation_videos, list_databases, list_schemas, list_tables, read_query). The naming is uniform, predictable, and uses snake_case throughout without any deviations.
With 7 tools, the server is well-scoped for Snowflake database operations. The count is appropriate, covering core functions like listing databases/schemas/tables, describing tables, executing queries, and adding insights, without being too sparse or bloated.
The toolset provides good coverage for Snowflake database interactions, including listing, describing, and querying. However, there are minor gaps such as lacking tools for creating or modifying database objects (e.g., create_table, update_table) or handling transactions, which agents might need to work around.
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