{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-snowflake","slug":"snowflake","name":"Snowflake","type":"mcp","url":"https://github.com/isaacwasserman/mcp-snowflake-server","page_url":"https://unfragile.ai/snowflake","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-snowflake__cap_0","uri":"capability://tool.use.integration.sql.read.query.execution.with.result.streaming","name":"sql read query execution with result streaming","description":"Executes SELECT queries against Snowflake databases through the MCP protocol, streaming results back to the client with automatic connection pooling and query timeout management. The server implements a database client layer that handles Snowflake connector initialization, query parsing, and result serialization into structured JSON responses. Queries are validated before execution to ensure they contain only SELECT operations.","intents":["I want Claude to query my Snowflake database and retrieve specific data without modifying anything","I need to fetch analytics data from Snowflake and have an AI assistant analyze the results","I want to enable read-only database access for AI agents without risking data modification"],"best_for":["Data analysts using Claude Desktop to explore Snowflake data","Teams building AI-powered analytics dashboards with read-only database access","Organizations requiring audit trails of database queries made by AI systems"],"limitations":["No query result pagination — entire result set must fit in memory and be serialized to JSON","Query timeout is fixed at server configuration time, not adjustable per-query","No support for streaming large result sets; all rows must be buffered before response","Result sets are limited by JSON serialization overhead and MCP message size constraints"],"requires":["Snowflake account with valid credentials (username/password or key pair)","Python 3.9+","snowflake-connector-python library","Network connectivity to Snowflake cloud infrastructure","MCP-compatible client (Claude Desktop, or custom MCP host)"],"input_types":["SQL SELECT query string","database name (string)","schema name (string)"],"output_types":["JSON array of row objects","query execution metadata (row count, execution time)","error messages with SQL syntax details"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_1","uri":"capability://tool.use.integration.sql.write.query.execution.with.permission.gating","name":"sql write query execution with permission gating","description":"Executes INSERT, UPDATE, DELETE, and CREATE TABLE operations against Snowflake when explicitly enabled via the --allow-write flag. The server implements a SQL write detector that parses query strings to identify write operations, then gates execution based on runtime configuration. Write operations are logged and tracked separately from read operations for audit purposes.","intents":["I want Claude to insert processed data back into Snowflake after analysis","I need to enable AI agents to create temporary tables for intermediate computations","I want to allow controlled data mutations while maintaining an audit trail of AI-initiated changes"],"best_for":["Data engineering teams using Claude to automate ETL pipeline steps","Organizations building AI-driven data transformation workflows","Teams that need explicit control over when AI systems can modify production data"],"limitations":["Write operations are disabled by default and require explicit --allow-write flag at server startup","No transaction support — each write query executes independently without rollback capability","Write detector uses string pattern matching, not full SQL parsing, so complex nested queries may be misclassified","No row-level or column-level access control — all write permissions are all-or-nothing","No built-in data validation or schema enforcement before writes"],"requires":["Snowflake account with INSERT, UPDATE, DELETE, and CREATE TABLE permissions","Python 3.9+","snowflake-connector-python library","Server started with --allow-write flag","MCP-compatible client with explicit user consent for write operations"],"input_types":["SQL INSERT/UPDATE/DELETE/CREATE query string","database name (string)","schema name (string)"],"output_types":["number of rows affected","query execution metadata","error messages with constraint violation details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_2","uri":"capability://search.retrieval.database.and.schema.discovery.with.hierarchical.listing","name":"database and schema discovery with hierarchical listing","description":"Provides tools to enumerate available databases, schemas within databases, and tables within schemas through a hierarchical traversal API. The server prefetches schema metadata at startup (if enabled) and caches it in memory, allowing fast schema exploration without repeated database round-trips. Each listing operation returns structured metadata including table names, column names, and data types.","intents":["I want Claude to explore my Snowflake database structure to understand what data is available","I need to help Claude understand the schema before it writes queries","I want to provide context about available tables without exposing sensitive data"],"best_for":["Teams onboarding Claude to new Snowflake databases","Data exploration workflows where the AI needs to discover available tables","Organizations using Claude as a natural language interface to Snowflake"],"limitations":["Schema metadata is cached at server startup — schema changes require server restart to reflect","No support for dynamic schema filtering per-query; exclusion patterns are static at configuration time","Metadata caching adds memory overhead proportional to database size","No support for listing views, materialized views, or external tables — only base tables","Column-level metadata (constraints, indexes, comments) is not exposed"],"requires":["Snowflake account with USAGE permission on databases and schemas","Python 3.9+","snowflake-connector-python library","Optional: --prefetch-schemas flag to enable startup caching"],"input_types":["database name (string, optional for list_databases)","schema name (string, optional for list_schemas)"],"output_types":["JSON array of database names","JSON array of schema names with metadata","JSON array of table names with column definitions"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_3","uri":"capability://search.retrieval.table.schema.introspection.with.column.level.details","name":"table schema introspection with column-level details","description":"Provides detailed column-level metadata for specific tables, including column names, data types, nullable constraints, and default values. The describe_table tool executes DESCRIBE TABLE queries against Snowflake and formats the results into a structured schema representation. This metadata is used by Claude to generate type-safe SQL queries and understand data semantics.","intents":["I want Claude to understand the exact column structure before writing queries","I need to provide Claude with data type information to prevent type mismatches","I want Claude to know which columns are nullable before generating INSERT statements"],"best_for":["AI-assisted SQL query generation where type safety is important","Data validation workflows where Claude needs to understand column constraints","Teams using Claude to generate data transformation code"],"limitations":["Does not expose foreign key relationships or referential constraints","Does not include index information or column statistics","Does not expose column-level comments or documentation","No support for querying constraints beyond nullability","Metadata is point-in-time and does not reflect schema changes until next query"],"requires":["Snowflake account with SELECT permission on target table","Python 3.9+","snowflake-connector-python library"],"input_types":["table name (string)","schema name (string, optional)","database name (string, optional)"],"output_types":["JSON object with column definitions","column names, data types, nullable flags","default values where applicable"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_4","uri":"capability://memory.knowledge.data.insight.accumulation.and.memo.management","name":"data insight accumulation and memo management","description":"Provides an append_insight tool that allows Claude to accumulate observations and findings about data into a persistent memo resource (memo://insights). The memo is stored in memory during the session and can be referenced in subsequent queries and analysis. This creates a working memory for multi-step data exploration where Claude can record intermediate findings and build on them.","intents":["I want Claude to remember key findings from earlier queries and reference them in later analysis","I need Claude to maintain a running log of data quality issues discovered during exploration","I want to capture Claude's analytical insights in a structured format for later review"],"best_for":["Multi-step data exploration workflows where Claude needs to track findings","Data quality assessment processes where insights accumulate over time","Teams using Claude for exploratory data analysis with audit trails"],"limitations":["Memo is stored in server memory only — not persisted to disk or database","No structured schema for insights — entries are free-form text","No versioning or history tracking — only current memo state is available","Memo is cleared when server restarts","No access control on memo — all clients connected to server can read/write insights","No full-text search or filtering of insights"],"requires":["Python 3.9+","MCP-compatible client","No database permissions required (memo is server-side only)"],"input_types":["insight text (string)","optional metadata (key-value pairs)"],"output_types":["confirmation of insight appended","current memo contents as text","memo as MCP resource (memo://insights)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_5","uri":"capability://safety.moderation.sql.write.operation.detection.and.enforcement","name":"sql write operation detection and enforcement","description":"Implements a SQL write detector component that analyzes query strings to identify INSERT, UPDATE, DELETE, CREATE, ALTER, and DROP operations before they reach the database. The detector uses pattern matching on SQL keywords and syntax to classify queries as read or write operations. This enforcement layer prevents write operations when the server is running in read-only mode (default), even if the Snowflake user account has write permissions.","intents":["I want to safely expose Snowflake to Claude without risking accidental data modification","I need to prevent write operations at the application layer before they reach the database","I want to enforce read-only access even if the database user has write permissions"],"best_for":["Organizations with strict data governance requiring application-level write prevention","Teams using Claude for data exploration where write access is not needed","Scenarios where database-level role management is not sufficient"],"limitations":["Write detection uses string pattern matching, not full SQL parsing — complex queries may be misclassified","Does not detect write operations hidden in stored procedures or functions","Cannot distinguish between different types of write operations for granular control","False positives possible with comments or string literals containing SQL keywords","No support for detecting indirect writes through views or triggers"],"requires":["Python 3.9+","No external dependencies beyond standard library"],"input_types":["SQL query string"],"output_types":["boolean (is_write_operation)","classification (SELECT, INSERT, UPDATE, DELETE, CREATE, etc.)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_6","uri":"capability://tool.use.integration.mcp.protocol.server.implementation.with.tool.and.resource.exposure","name":"mcp protocol server implementation with tool and resource exposure","description":"Implements a complete MCP server that exposes Snowflake capabilities as tools (callable functions) and resources (data references) through the Model Context Protocol. The server handles MCP client connections, request routing, tool invocation, and resource serving. It implements the MCP specification for both stdio and HTTP transports, allowing integration with Claude Desktop and other MCP-compatible clients.","intents":["I want to integrate Snowflake database access into Claude Desktop","I need to expose database tools to any MCP-compatible AI client","I want to standardize how AI systems interact with my Snowflake infrastructure"],"best_for":["Teams using Claude Desktop who want native Snowflake integration","Organizations building custom MCP clients that need database access","Infrastructure teams standardizing AI-database interaction patterns"],"limitations":["MCP server runs as a single process — no built-in clustering or load balancing","No authentication/authorization at the MCP protocol level — relies on client trust","Tool responses are serialized to JSON, limiting support for binary data or large result sets","No built-in rate limiting or request throttling at the MCP layer","Server state (memo, cached schemas) is not shared across multiple server instances"],"requires":["Python 3.9+","mcp library (Model Context Protocol SDK)","Claude Desktop 0.1.0+ (for Claude Desktop integration)","Or any MCP-compatible client"],"input_types":["MCP tool calls with parameters","MCP resource requests"],"output_types":["MCP tool results (JSON)","MCP resources (text/JSON)","MCP error responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_7","uri":"capability://automation.workflow.snowflake.connection.pooling.and.credential.management","name":"snowflake connection pooling and credential management","description":"Manages Snowflake database connections through a connection pool that reuses connections across multiple queries, reducing connection overhead. The server loads Snowflake credentials from environment variables (SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, SNOWFLAKE_ACCOUNT, etc.) or command-line arguments, and initializes the Snowflake connector with these credentials. Connection parameters are validated at startup to fail fast if credentials are invalid.","intents":["I want to efficiently manage database connections without creating a new connection per query","I need to securely pass Snowflake credentials to the MCP server","I want to validate credentials at startup to catch configuration errors early"],"best_for":["Production deployments where connection efficiency is important","Teams using environment-based credential management","Organizations requiring secure credential handling"],"limitations":["Connection pool size is fixed at server startup — not adjustable per-query","No support for connection timeout or idle connection cleanup","Credentials are loaded from environment variables or command-line arguments — no support for credential rotation","No built-in connection monitoring or health checks","Connection pool is not shared across multiple server instances"],"requires":["Python 3.9+","snowflake-connector-python library","Snowflake account credentials (username, password or key pair, account identifier)","Environment variables: SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, SNOWFLAKE_ACCOUNT, SNOWFLAKE_DATABASE, SNOWFLAKE_SCHEMA"],"input_types":["environment variables or command-line arguments"],"output_types":["validated Snowflake connection object","connection status and metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-snowflake__cap_8","uri":"capability://safety.moderation.configurable.database.and.schema.exclusion.filtering","name":"configurable database and schema exclusion filtering","description":"Supports filtering of databases, schemas, and tables through exclusion patterns specified in configuration. The server can be configured to hide sensitive databases or schemas from discovery operations (list_databases, list_schemas, list_tables) without requiring changes to Snowflake role permissions. Exclusion patterns are evaluated at query time against the full hierarchical path.","intents":["I want to hide sensitive databases from Claude without modifying Snowflake permissions","I need to restrict Claude's access to specific schemas while using the same database user","I want to prevent accidental queries against production tables during testing"],"best_for":["Organizations with mixed sensitive and non-sensitive data in the same Snowflake account","Teams using Claude for development/testing who need to exclude production databases","Scenarios where database-level role management is not flexible enough"],"limitations":["Exclusion patterns are static at server startup — cannot be changed without restart","Patterns are simple string matching, not regex or glob patterns","Exclusions apply only to discovery operations (list_*) — direct queries to excluded tables are not blocked","No support for row-level or column-level filtering","Exclusion configuration is not exposed through the MCP interface"],"requires":["Python 3.9+","Configuration file or environment variables specifying exclusion patterns"],"input_types":["exclusion pattern strings (database names, schema names, table names)"],"output_types":["filtered list of databases/schemas/tables"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Snowflake account with valid credentials (username/password or key pair)","Python 3.9+","snowflake-connector-python library","Network connectivity to Snowflake cloud infrastructure","MCP-compatible client (Claude Desktop, or custom MCP host)","Snowflake account with INSERT, UPDATE, DELETE, and CREATE TABLE permissions","Server started with --allow-write flag","MCP-compatible client with explicit user consent for write operations","Snowflake account with USAGE permission on databases and schemas","Optional: --prefetch-schemas flag to enable startup caching"],"failure_modes":["No query result pagination — entire result set must fit in memory and be serialized to JSON","Query timeout is fixed at server configuration time, not adjustable per-query","No support for streaming large result sets; all rows must be buffered before response","Result sets are limited by JSON serialization overhead and MCP message size constraints","Write operations are disabled by default and require explicit --allow-write flag at server startup","No transaction support — each write query executes independently without rollback capability","Write detector uses string pattern matching, not full SQL parsing, so complex nested queries may be misclassified","No row-level or column-level access control — all write permissions are all-or-nothing","No built-in data validation or schema enforcement before writes","Schema metadata is cached at server startup — schema changes require server restart to reflect","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=snowflake","compare_url":"https://unfragile.ai/compare?artifact=snowflake"}},"signature":"LnPQ7hwEdZWGI22OY743s9sc0acJ5vK7JhI5hZTqXp/EFopATZOGPDipwzzwbWkDFW1rCC4KXIaaSBdZbcqvBg==","signedAt":"2026-06-22T18:25:47.583Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/snowflake","artifact":"https://unfragile.ai/snowflake","verify":"https://unfragile.ai/api/v1/verify?slug=snowflake","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}