Which integrations are available for this server?

Q: Which integrations are available for this server?

Connect AI assistants to Snowflake — enabling SQL queries, schema exploration, and data insights directly from your LLM client.

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MCP Snowflake Server NSP

by nsphung

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Snowflake MCP Server NSP

A Model Context Protocol (MCP) server / MCP server that connects AI assistants to Snowflake — enabling SQL queries, schema exploration, and data insights directly from your LLM client.

Highlights:

Multiple authentication methods: password, key-pair, external browser, OAuth 2.0 (client credentials & bearer token), TOML connection files
TOML multi-connection config — manage production, staging, and development environments in one file
Write-safety guard — write operations are disabled by default and must be explicitly enabled
Exclusion patterns — filter out databases, schemas, or tables from discovery
--exclude-json-results flag — reduces LLM context window usage
Selective tool exclusion via --exclude_tools
Prefetch mode — pre-load table schema as MCP resources
Docker support with hardened image (DHI, nonroot user, no shell in runtime)

Snowflake MCP Server NSP

Related MCP server: CentralMind/Gateway

Quick Start

The fastest way to try it — using uvx with a TOML connection file:

# 1. Create a connections file
cat > ~/snowflake_connections.toml << 'EOF'
[myconn]
account = "your_account"
user = "your_user"
password = "your_password"
warehouse = "COMPUTE_WH"
database = "MY_DB"
schema = "PUBLIC"
role = "MYROLE"
EOF

# 2. Run the server
uvx --python=3.13 --from mcp-snowflake-server-nsp mcp_snowflake_server \
  --connections-file ~/snowflake_connections.toml \
  --connection-name myconn

Claude Code

Add to your MCP client config (e.g. claude_desktop_config.json) using snowflake_connections.toml:

"mcpServers": {
  "snowflake": {
    "command": "uvx",
    "args": [
      "--python=3.13",
      "--from", "mcp-snowflake-server-nsp",
      "mcp_snowflake_server",
      "--connections-file", "/absolute/path/to/snowflake_connections.toml",
      "--connection-name", "myconn"
    ]
  }
}

Visual Studio Code (VSCode)

uvx — Install in VS Code Install in VS Code Insiders

Docker — Install in VS Code (Docker) Install in VS Code Insiders (Docker)

Or add manually to your MCP client config (e.g. .vscode/mcp.json) using .env file (see Authentication):

"snowflake": {
      // Snowflake MCP server
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from", "mcp-snowflake-server-nsp",
        "--python=3.13",
        "mcp_snowflake_server"
      ],
      "envFile": "${workspaceFolder}/.env"
    }

OpenCode

Add to your MCP client config (e.g. opencode.jsonc) with .env file (see Authentication):

"snowflake": {
  "type": "local",
  "command": [
    "uvx",
    "--from",
    "mcp-snowflake-server-nsp",
    "--python=3.13",
    "mcp_snowflake_server",
  ],
  "enabled": true,
  "timeout": 300000,
}

Components

Resources

URI	Description
`memo://insights`	A continuously updated memo aggregating data insights appended via `append_insight`.
`context://table/{table_name}`	(Prefetch mode only) Per-table schema summaries including columns and comments.

Tools

Query Tools

Tool	Description	Requires
`read_query`	Execute `SELECT` queries. Input: `query` (string).	—
`write_query`	Execute `INSERT`, `UPDATE`, or `DELETE` queries. Input: `query` (string).	`--allow_write`
`create_table`	Execute `CREATE TABLE` statements. Input: `query` (string).	`--allow_write`

Schema Tools

Tool	Description	Input
`list_databases`	List all databases in the Snowflake instance.	—
`list_schemas`	List all schemas within a database.	`database` (string)
`list_tables`	List all tables within a database and schema.	`database`, `schema` (strings)
`describe_table`	Describe columns of a table (name, type, nullability, default, comment).	`table_name` as `database.schema.table`

Analysis Tools

Tool	Description	Input
`append_insight`	Add a data insight to the `memo://insights` resource.	`insight` (string)

Authentication

Password

Set credentials via environment variables or CLI flags (see Configuration Reference):

SNOWFLAKE_USER="user@example.com"
SNOWFLAKE_ACCOUNT="myaccount"
SNOWFLAKE_AUTHENTICATOR="snowflake"
SNOWFLAKE_PASSWORD="secret"
SNOWFLAKE_WAREHOUSE="COMPUTE_WH"
SNOWFLAKE_DATABASE="MY_DB"
SNOWFLAKE_SCHEMA="PUBLIC"
SNOWFLAKE_ROLE="MYROLE"

Key-Pair

Both RSA (RS256) and ECDSA (ES256, ES384, ES512) private keys are supported (requires snowflake-connector-python ≥ 4.5.0 for ECDSA).

SNOWFLAKE_USER="user@example.com"
SNOWFLAKE_ACCOUNT="myaccount"
SNOWFLAKE_AUTHENTICATOR="snowflake_jwt"
SNOWFLAKE_PRIVATE_KEY_FILE="/absolute/path/to/key.p8"
SNOWFLAKE_PRIVATE_KEY_FILE_PWD="passphrase"  # Optional — only if key is encrypted
SNOWFLAKE_WAREHOUSE="COMPUTE_WH"
SNOWFLAKE_DATABASE="MY_DB"
SNOWFLAKE_SCHEMA="PUBLIC"
SNOWFLAKE_ROLE="MYROLE"

Or via CLI: --private_key_file /path/to/key.p8 --private_key_file_pwd passphrase

External Browser

SNOWFLAKE_AUTHENTICATOR="externalbrowser"

Or in a TOML connection entry: authenticator = "externalbrowser"

OAuth 2.0 Client Credentials

Use the OAuth 2.0 client credentials flow to authenticate with a client ID and secret (no user interaction required):

SNOWFLAKE_AUTHENTICATOR="oauth_client_credentials"
SNOWFLAKE_ACCOUNT="myaccount"
SNOWFLAKE_OAUTH_CLIENT_ID="your_client_id"
SNOWFLAKE_OAUTH_CLIENT_SECRET="your_client_secret"
SNOWFLAKE_OAUTH_TOKEN_REQUEST_URL="https://your-idp.example.com/oauth/token"
SNOWFLAKE_OAUTH_SCOPE="session:role:MY_ROLE"  # Optional
SNOWFLAKE_WAREHOUSE="COMPUTE_WH"
SNOWFLAKE_DATABASE="MY_DB"
SNOWFLAKE_SCHEMA="PUBLIC"
SNOWFLAKE_ROLE="MYROLE"

OAuth Bearer Token

Use a pre-fetched OAuth bearer token:

SNOWFLAKE_AUTHENTICATOR="oauth"
SNOWFLAKE_ACCOUNT="myaccount"
SNOWFLAKE_TOKEN="eyJhbGciOiJSUzI1NiJ9..."
SNOWFLAKE_WAREHOUSE="COMPUTE_WH"
SNOWFLAKE_DATABASE="MY_DB"
SNOWFLAKE_SCHEMA="PUBLIC"
SNOWFLAKE_ROLE="MYROLE"

TOML Connection File (Recommended)

Manage multiple environments in a single file. See example_connections.toml for a full template.

[production]
account = "your_account"
user = "your_user"
password = "your_password"
authenticator = "snowflake"
warehouse = "COMPUTE_WH"
database = "PROD_DB"
schema = "PUBLIC"
role = "ACCOUNTADMIN"

[development]
account = "your_account"
user = "dev_user"
authenticator = "externalbrowser"
warehouse = "DEV_WH"
database = "DEV_DB"
schema = "PUBLIC"
role = "DEVELOPER"

[reporting]
account = "your_account"
user = "reporting_user"
authenticator = "snowflake_jwt"
private_key_file = "/path/to/private_key.pem"
private_key_file_pwd = "passphrase"  # Optional
warehouse = "REPORTING_WH"
database = "REPORTING_DB"
schema = "REPORTS"
role = "REPORTING_ROLE"

[analytics_oauth]
account = "your_account"
authenticator = "oauth_client_credentials"
oauth_client_id = "your_client_id"
oauth_client_secret = "your_client_secret"
oauth_token_request_url = "https://your-idp.example.com/oauth/token"
oauth_scope = "session:role:ANALYTICS_ROLE"  # Optional
warehouse = "ANALYTICS_WH"
database = "ANALYTICS_DB"
schema = "PUBLIC"
role = "ANALYTICS_ROLE"

Pass the file with --connections-file and select a profile with --connection-name. Both flags are required together.

Installation

The package is published on PyPI as mcp-snowflake-server-nsp.

Contributing or running from source? See CONTRIBUTING.md for local development setup, test commands, formatting, and building the Docker image from source.

Via UVX

"mcpServers": {
  "snowflake_production": {
    "command": "uvx",
    "args": [
      "--python=3.13",
      "--from", "mcp-snowflake-server-nsp",
      "mcp_snowflake_server",
      "--connections-file", "/path/to/snowflake_connections.toml",
      "--connection-name", "production"
      // Optional flags — see Configuration Reference
    ]
  },
  "snowflake_staging": {
    "command": "uvx",
    "args": [
      "--python=3.13",
      "--from", "mcp-snowflake-server-nsp",
      "mcp_snowflake_server",
      "--connections-file", "/path/to/snowflake_connections.toml",
      "--connection-name", "staging"
    ]
  }
}

"mcpServers": {
  "snowflake": {
    "command": "uvx",
    "args": [
      "--python=3.13",
      "--from", "mcp-snowflake-server-nsp",
      "mcp_snowflake_server",
      "--account", "your_account",
      "--warehouse", "your_warehouse",
      "--user", "your_user",
      "--password", "your_password",
      "--role", "your_role",
      "--database", "your_database",
      "--schema", "your_schema"
      // Optional: "--private_key_file", "/absolute/path/key.p8"
      // Optional: "--private_key_file_pwd", "passphrase"
      // Optional flags — see Configuration Reference
    ]
  }
}

Via Docker Hub

The image is published on Docker Hub — no build step required:

docker pull nsphung/mcp-snowflake-server-nsp

Note: -i (--interactive) is required to keep stdin open for the MCP stdio transport. Do not use -d (detach).

With .env file (see Authentication):

"mcpServers": {
  "snowflake": {
    "command": "docker",
    "args": [
      "run", "--rm", "-i",
      "--env-file", "/absolute/path/to/.env",
      "nsphung/mcp-snowflake-server-nsp"
    ]
  }
}

With TOML connections file:

"mcpServers": {
  "snowflake": {
    "command": "docker",
    "args": [
      "run", "--rm", "-i",
      "-v", "/path/to/snowflake_connections.toml:/app/snowflake_connections.toml:ro",
      "nsphung/mcp-snowflake-server-nsp",
      "--connections-file", "/app/snowflake_connections.toml",
      "--connection-name", "production"
    ]
  }
}

With .env file:

"snowflake": {
  "type": "stdio",
  "command": "docker",
  "args": [
    "run", "--rm", "-i",
    "nsphung/mcp-snowflake-server-nsp"
  ],
  "envFile": "${workspaceFolder}/.env"
}

With TOML connections file:

"snowflake": {
  "type": "stdio",
  "command": "docker",
  "args": [
    "run", "--rm", "-i",
    "-v", "/path/to/snowflake_connections.toml:/app/snowflake_connections.toml:ro",
    "nsphung/mcp-snowflake-server-nsp",
    "--connections-file", "/app/snowflake_connections.toml",
    "--connection-name", "production"
  ]
}

"snowflake": {
  "type": "local",
  "command": [
    "docker", "run", "--rm", "-i",
    "--env-file", "/absolute/path/to/.env",
    "nsphung/mcp-snowflake-server-nsp"
  ],
  "enabled": true,
  "timeout": 300000
}

Configuration Reference

All connection parameters can also be set as environment variables (SNOWFLAKE_<PARAM_UPPER>).

Flag	Env var	Default	Description
`--account`	`SNOWFLAKE_ACCOUNT`	—	Snowflake account identifier
`--user`	`SNOWFLAKE_USER`	—	Snowflake username
`--password`	`SNOWFLAKE_PASSWORD`	—	Password (not required for key-pair / SSO)
`--warehouse`	`SNOWFLAKE_WAREHOUSE`	—	Virtual warehouse to use
`--database`	`SNOWFLAKE_DATABASE`	(required)	Default database
`--schema`	`SNOWFLAKE_SCHEMA`	(required)	Default schema
`--role`	`SNOWFLAKE_ROLE`	—	Role to assume
`--private_key_file`	`SNOWFLAKE_PRIVATE_KEY_FILE`	—	Absolute path to RSA or ECDSA (ES256/384/512) private key file (`.p8` / `.pem`)
`--private_key_file_pwd`	`SNOWFLAKE_PRIVATE_KEY_FILE_PWD`	—	Passphrase for encrypted private key
`--connections-file`	—	—	Path to TOML connections file
`--connection-name`	—	—	Connection profile name in TOML file (required with `--connections-file`)
`--allow_write`	—	`false`	Enable `write_query` and `create_table` tools
`--prefetch` / `--no-prefetch`	—	`false`	Pre-load table schema as `context://table/*` resources (disables `list_tables` / `describe_table`)
`--exclude_tools`	—	`[]`	Space-separated list of tool names to disable
`--exclude-json-results`	—	`false`	Omit embedded JSON resources from responses (reduces context window usage)
`--log_dir`	—	—	Directory for log file output
`--log_level`	—	`INFO`	Log verbosity: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`

Exclusion Patterns

Edit runtime_config.json to exclude databases, schemas, or tables from all discovery tools. Patterns are matched case-insensitively as substrings.

{
  "exclude_patterns": {
    "databases": ["temp"],
    "schemas": ["temp", "information_schema"],
    "tables": ["temp"]
  }
}

The server loads this file automatically at startup from the working directory.

License

This project is licensed under the MIT License. See the LICENSE file for the full text.

Fork and Attribution

This repository is a fork of isaacwasserman/mcp-snowflake-server.

Upstream authors and contributors retain copyright for their contributions.
Fork-specific changes are maintained by nsphung.
A summary of notable modifications is tracked in NOTICE.

Available Tools

6 tools

append_insightB

Add a data insight to the memo

ParametersJSON Schema

Name	Required	Description	Default
`insight`	Yes	Data insight discovered from analysis

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden for behavioral traits but only states the action. It does not disclose side effects (e.g., whether the memo is modified permanently), permissions needed, or return behavior beyond 'add'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no unnecessary words. It is front-loaded and concise for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, the description is mostly adequate. It identifies the action and parameter, though it could be more explicit about which memo (e.g., current context) and whether it appends or replaces.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'insight' has a description in the schema ('Data insight discovered from analysis'), and schema description coverage is 100%. The tool description adds no further meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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 resource ('data insight to the memo'), with a specific verb and resource that distinguishes it from sibling tools (e.g., describe_table, read_query).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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, nor any context about prerequisites or exclusions. The description only states the function without usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

describe_tableA

Get the schema information for a specific table

ParametersJSON Schema

Name	Required	Description	Default
`table_name`	Yes	Fully qualified table name in the format 'database.schema.table'

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description indicates a safe read operation. However, with no annotations, it lacks details on potential limitations, permissions, or the exact content of the schema returned (e.g., columns, types).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words. It is appropriately sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has one parameter and no output schema, the description is mostly complete. However, it could specify what 'schema information' includes (e.g., columns, types) for better agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%; the parameter 'table_name' is fully described in the schema. The description adds no additional semantic meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get the schema information for a specific table' clearly specifies the verb and resource. It distinguishes from sibling tools like list_tables which only list table names.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates usage for retrieving schema of a specific table. While no explicit when-not-to-use is provided, sibling names provide context, making it clear that this tool is for detailed schema rather than listing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_databasesA

List all available databases in Snowflake

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. The description only states it lists databases, but does not disclose behavioral details such as permissions required, whether it returns names only, or any limitations. For a read-only tool, minimal disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence of 6 words, no redundancy, front-loaded. Every word is necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with no parameters and no output schema, the description is minimally adequate. It could mention return format or whether it requires any permissions, but overall meets basic needs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters in schema, so description doesn't need to add parameter semantics. Baseline for 0 parameters is 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all available databases in Snowflake, with a specific verb ('List') and resource ('databases'). It distinguishes itself from sibling tools like 'list_schemas' and 'describe_table'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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'. No when-not-to-use or explicit context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_schemasB

List all schemas in a database

ParametersJSON Schema

Name	Required	Description	Default
`database`	Yes	Database name to list schemas from

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description only states the action without disclosing any behavioral traits like authentication, side effects, or return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded and concise without wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool, the description and schema together provide adequate information, though no output schema exists to explain return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and parameter description in schema is clear; description adds no extra semantic value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'List' and resource 'schemas', clearly distinguishing from siblings like list_databases and list_tables.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives; no context about prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_tablesB

List all tables in a specific database and schema

ParametersJSON Schema

Name	Required	Description	Default
`schema`	Yes	Schema name
`database`	Yes	Database name

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Only states 'list', implying read-only, but no details on permissions, performance, or whether metadata is included. Insufficient for a tool with zero annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with no wasted words. Front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with full schema coverage and no output schema, the description is adequate. Lacks context about output format but sufficient for basic usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions. Description adds no extra meaning beyond what schema already provides; baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'List all tables in a specific database and schema' with a specific verb and resource. It distinguishes from sibling tools like list_databases and list_schemas which cover different resources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives like list_databases, list_schemas, or describe_table. No mention of prerequisites (e.g., need to select database first) or when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

read_queryB

Execute a SELECT query.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	SELECT SQL query to execute

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only says 'Execute a SELECT query.' This implies a read-only operation, but it does not explicitly state that no side effects occur, no data is modified, or what happens on invalid queries. The behavioral profile is insufficiently disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at 4 words, front-loading the core purpose. While it could be slightly more structured by adding a sentence about return value or constraints, it is not verbose and wastes no words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and a single parameter, the description is minimally adequate: it states what the tool does. However, it lacks mention of the return format (e.g., result rows), potential limits (e.g., max rows), or error behavior. It is complete enough for a simple tool but could be more informative.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with the query parameter description 'SELECT SQL query to execute'. The tool description adds no extra meaning beyond this. Per guidelines, baseline is 3 when coverage is high, and there is no additional value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Execute' and the resource 'SELECT query', making the tool's function obvious. It is distinct from sibling tools like describe_table or list_tables, which are metadata operations, and append_insight, which is likely a write operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 does not mention that it is for ad-hoc data retrieval and not for exploring schema or modifying data, 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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

6 tool updatesv0.14.0Jul 9, 2026
- First observedappend_insight
- First observeddescribe_table
- First observedlist_databases
- First observedlist_schemas
- First observedlist_tables
- First observedread_query

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.7/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: listing databases, schemas, tables, describing a table's schema, executing queries, and appending a text insight. There is no ambiguity or overlap between them.

Naming Consistency5/5

All tool names follow the consistent verb_noun pattern with lowercase and underscores (e.g., list_databases, describe_table, append_insight). The naming is uniform and predictable.

Tool Count5/5

With 6 tools, the set is well-scoped for a Snowflake exploration and insight server. It covers basic metadata discovery, querying, and an insight-add feature without being bloated or insufficient.

Completeness3/5

The tool set covers read operations and metadata listing well, but lacks write capabilities beyond appending insights (no INSERT/UPDATE/DELETE queries, no DDL operations like creating tables). This creates a notable gap for many database workflows.

Maintenance

ActivityActive

ResponsivenessWithin a week

Resources

GitHub Repository

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Snowflake MCP Server NSP

Table of Contents

Quick Start

Claude Code

Visual Studio Code (VSCode)

OpenCode

Components

Resources

Tools

Query Tools

Schema Tools

Analysis Tools

Authentication

Password

Key-Pair

External Browser

OAuth 2.0 Client Credentials

OAuth Bearer Token

TOML Connection File (Recommended)

Installation

Via UVX

Via Docker Hub

Configuration Reference

Exclusion Patterns

License

Fork and Attribution

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

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