Eine Model Context Protocol (MCP)-Serverimplementierung, die die Datenbankinteraktion mit Snowflake ermöglicht. Dieser Server ermöglicht die Ausführung von SQL-Abfragen über Tools und stellt Dateneinblicke und Schemakontext als Ressourcen bereit.
Related MCP server: Snowflake MCP Server
memo://insights
Ein ständig aktualisiertes Memo, das die gewonnenen Datenerkenntnisse zusammenfasst.
Wird automatisch aktualisiert, wenn neue Erkenntnisse über das Tool append_insight angehängt werden.
context://table/{table_name}
(Wenn Prefetch aktiviert ist) Schemazusammenfassungen pro Tabelle, einschließlich Spalten und Kommentaren, werden als einzelne Ressourcen angezeigt.
Der Server stellt die folgenden Tools bereit:
read_query
Führen Sie SELECT -Abfragen aus, um Daten aus der Datenbank zu lesen.
Eingang:
query (Zeichenfolge): Die auszuführende SELECT SQL-Abfrage
Gibt zurück: Abfrageergebnisse als Array von Objekten
write_query (nur aktiviert mit --allow-write )
Führen Sie INSERT , UPDATE oder DELETE -Abfragen aus.
Eingang:
query (Zeichenfolge): Die SQL-Änderungsabfrage
Rückgabe: Anzahl der betroffenen Zeilen oder Bestätigung
create_table (nur aktiviert mit --allow-write )
Erstellen Sie neue Tabellen in der Datenbank.
Eingang:
query (Zeichenfolge): CREATE TABLE SQL-Anweisung
Rückgabe: Bestätigung der Tabellenerstellung
list_databases
Listen Sie alle Datenbanken in der Snowflake-Instanz auf.
Gibt zurück: Array von Datenbanknamen
list_schemas
Listet alle Schemata innerhalb einer bestimmten Datenbank auf.
Eingang:
database (Zeichenfolge): Name der Datenbank
Gibt zurück: Array von Schemanamen
list_tables
Listet alle Tabellen innerhalb einer bestimmten Datenbank und eines bestimmten Schemas auf.
Eingang:
database (Zeichenfolge): Name der Datenbank
schema (Zeichenfolge): Name des Schemas
Gibt zurück: Array von Tabellenmetadaten
describe_table
Zeigen Sie Spalteninformationen für eine bestimmte Tabelle an.
Eingang:
table_name (Zeichenfolge): Vollqualifizierter Tabellenname ( database.schema.table )
Rückgabe: Array von Spaltendefinitionen mit Namen, Typen, Nullbarkeit, Standardwerten und Kommentaren
append_insight
Fügen Sie der Memoressource neue Dateneinblicke hinzu.
Eingang:
insight (Zeichenfolge): Durch Analyse gewonnene Dateneinblicke
Rücksendungen: Bestätigung der Erkenntnisaufnahme
Effekt: Löst eine Aktualisierung der Ressource memo://insights aus
So installieren Sie Snowflake Server für Claude Desktop automatisch über 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}"]
]
}
}Installieren Sie die Claude AI Desktop App
Installieren Sie uv :
curl -LsSf https://astral.sh/uv/install.sh | shErstellen Sie eine .env Datei mit Ihren Snowflake-Anmeldeinformationen:
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] Ändern Sie runtime_config.json , um Ausschlussmuster für Datenbanken, Schemas oder Tabellen festzulegen.
Lokal testen:
uv --directory /absolute/path/to/mcp_snowflake_server run mcp_snowflake_serverFügen Sie den Server zu Ihrer claude_desktop_config.json hinzu:
"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}"]
]
}
}Schreibvorgänge sind standardmäßig deaktiviert . Aktivieren Sie sie explizit mit --allow-write .
Der Server unterstützt das Herausfiltern bestimmter Datenbanken, Schemata oder Tabellen über Ausschlussmuster.
Wenn das Vorablesen aktiviert ist, stellt der Server zusätzliche Kontextressourcen pro Tabelle bereit.
Das Tool append_insight aktualisiert die Ressource memo://insights dynamisch.
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, 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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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 all available databases in Snowflake
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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.
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.
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.
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.
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.
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 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?
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.
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.
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.
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.
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.
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 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 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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
append_insightdescribe_tablelist_databaseslist_schemaslist_tablesread_queryScored across 6 tools
Each tool targets a distinct operation: metadata listing, schema description, query execution, and memo insight. No overlap.
All tool names follow a consistent verb_noun pattern with lowercase and underscores, e.g., list_databases, read_query.
6 tools is well-scoped for a Snowflake server covering metadata exploration and querying without being excessive.
Covers metadata listing and SELECT queries thoroughly, but lacks DML/DDL operations like INSERT or CREATE, which may be needed depending on use case.
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