{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-starrocks","slug":"starrocks","name":"StarRocks","type":"mcp","url":"https://github.com/StarRocks/mcp-server-starrocks","page_url":"https://unfragile.ai/starrocks","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-starrocks__cap_0","uri":"capability://data.processing.analysis.read.only.sql.query.execution.with.result.streaming","name":"read-only sql query execution with result streaming","description":"Executes SELECT queries and read-only operations against StarRocks databases through the MCP protocol, returning structured result sets with automatic connection pooling and error handling. The implementation maintains a persistent global connection to avoid repeated connection overhead while supporting query timeouts and result formatting for AI assistant consumption.","intents":["I want to query my StarRocks database from an AI assistant without writing custom client code","I need to fetch data for analysis or context without managing database connections myself","I want to execute exploratory SELECT queries and get results formatted for LLM processing"],"best_for":["AI assistant builders integrating StarRocks as a data source","Teams using Claude, ChatGPT, or other MCP-compatible AI assistants for database queries","Data analysts building AI-powered analytics workflows"],"limitations":["Read-only operations only — cannot modify data or schema with this tool","Result sets are fully materialized in memory before returning — large result sets may cause memory pressure","No built-in pagination or streaming for very large result sets","Query timeout behavior depends on StarRocks server configuration, not client-side limits"],"requires":["StarRocks database instance running and accessible","Network connectivity to StarRocks host (default localhost:9030)","Valid database credentials via STARROCKS_USER and STARROCKS_PASSWORD environment variables","Python 3.8+ for MCP server runtime"],"input_types":["SQL query string (SELECT, SHOW, DESCRIBE statements)"],"output_types":["JSON-formatted result set with column names and row data","Error messages with SQL error details"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_1","uri":"capability://data.processing.analysis.write.query.execution.with.ddl.dml.support","name":"write-query execution with ddl/dml support","description":"Executes data modification operations (INSERT, UPDATE, DELETE, CREATE TABLE, ALTER TABLE, DROP) against StarRocks through MCP tools with automatic transaction handling and schema change propagation. The implementation validates write operations before execution and clears the in-memory overview cache to ensure subsequent reads reflect schema/data changes.","intents":["I want an AI assistant to help me create tables, indexes, or modify schema in StarRocks","I need to insert or update data in StarRocks based on AI-generated SQL","I want to automate data loading pipelines where an AI assistant generates and executes DDL/DML"],"best_for":["AI-assisted database schema design and migration workflows","Automated data ingestion pipelines controlled by AI agents","Teams building self-healing or self-optimizing database systems"],"limitations":["No transaction rollback support — failed writes may leave partial state changes","Cache invalidation is table-level only; related views or dependent tables are not automatically refreshed","No dry-run or validation mode — queries execute immediately without preview","DDL operations may lock tables; concurrent read queries could be blocked during schema changes"],"requires":["StarRocks database with write permissions for the configured user","STARROCKS_USER must have DDL/DML privileges on target tables/databases","Network connectivity to StarRocks host","Python 3.8+ for MCP server"],"input_types":["SQL DDL statements (CREATE, ALTER, DROP, TRUNCATE)","SQL DML statements (INSERT, UPDATE, DELETE)"],"output_types":["Execution status (success/failure)","Rows affected count","Error messages with constraint or syntax details"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_2","uri":"capability://memory.knowledge.database.and.table.schema.exploration.via.uri.resources","name":"database and table schema exploration via uri resources","description":"Exposes database and table metadata through MCP resource URIs (starrocks:///databases, starrocks:///{db}/tables, starrocks:///{db}/{table}/schema) that AI assistants can reference directly without tool calls. The implementation translates URI paths into SHOW/DESCRIBE queries and caches results to avoid repeated metadata queries, enabling efficient schema discovery in multi-turn conversations.","intents":["I want an AI assistant to understand my database structure without me describing it manually","I need the AI to list all tables in a database and their schemas as context for query generation","I want to reference table schemas in prompts without embedding SQL DESCRIBE statements"],"best_for":["AI assistants generating queries based on dynamic schema discovery","Teams with frequently-changing schemas that need real-time metadata reflection","Multi-turn conversations where schema context must be refreshed between turns"],"limitations":["Resources are read-only — schema modifications must use write_query tool, not resources","Cache is in-memory only; server restart clears all cached metadata","No support for schema versioning or historical metadata tracking","Large databases with hundreds of tables may have slow initial metadata enumeration"],"requires":["StarRocks database with READ permissions on information_schema","Network connectivity to StarRocks host","MCP client that supports resource URI resolution"],"input_types":["URI paths in format starrocks:///databases, starrocks:///{db}/tables, starrocks:///{db}/{table}/schema"],"output_types":["JSON-formatted list of database names","JSON-formatted list of table names with metadata","JSON-formatted schema definition with column names, types, and constraints"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_3","uri":"capability://data.processing.analysis.intelligent.table.and.database.overview.generation.with.sampling","name":"intelligent table and database overview generation with sampling","description":"Generates comprehensive summaries of tables and databases including schema definitions, row counts, and representative data samples through table_overview and db_overview tools. The implementation executes SHOW CREATE TABLE, COUNT(*), and LIMIT sampling queries, then caches results using (database_name, table_name) tuples to avoid redundant metadata/sampling queries across multiple AI assistant requests.","intents":["I want an AI assistant to understand table structure, size, and sample data in one call","I need quick summaries of all tables in a database for context without individual queries","I want the AI to see representative data samples to generate more accurate queries"],"best_for":["AI query generation systems that benefit from data distribution context","Teams building AI-powered data exploration tools","Scenarios where AI assistants need to understand data semantics beyond schema"],"limitations":["Sampling is random/first-N rows only — does not capture data distribution or outliers","Row count is exact COUNT(*) which can be slow on very large tables (billions of rows)","Cache has no TTL or invalidation strategy beyond write_query operations","db_overview on databases with 100+ tables may timeout or consume significant memory"],"requires":["StarRocks database with READ permissions on target tables","Sufficient query timeout to complete COUNT(*) on large tables","Network connectivity to StarRocks host","Python 3.8+ for MCP server"],"input_types":["Database name (string)","Table name (string)"],"output_types":["JSON object containing: schema definition, row count, sample rows, column statistics"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_4","uri":"capability://image.visual.query.driven.data.visualization.with.plotly.chart.generation","name":"query-driven data visualization with plotly chart generation","description":"Executes a SQL query and automatically generates interactive Plotly charts from the result set through the query_and_plotly_chart tool. The implementation detects numeric and categorical columns, infers appropriate chart types (bar, line, scatter, pie), and returns both raw query results and embedded Plotly JSON for rendering in AI assistant interfaces or web frontends.","intents":["I want an AI assistant to generate both data and visualizations from a single query","I need to create dashboards where an AI assistant generates queries and charts automatically","I want to explore data visually without manually configuring chart parameters"],"best_for":["AI-powered analytics dashboards and exploration tools","Teams building self-service BI systems with AI query generation","Scenarios where visual data exploration accelerates decision-making"],"limitations":["Chart type inference is heuristic-based — may select suboptimal visualizations for complex data","No support for multi-series or faceted charts — single chart per query result","Large result sets (>10k rows) may produce cluttered or slow-rendering charts","Chart customization (colors, labels, axes) is not configurable via the tool"],"requires":["StarRocks database with READ permissions","Query that returns at least 2 columns (one for X-axis, one for Y-axis or values)","Plotly library installed in MCP server environment","Client capable of rendering Plotly JSON (web browser, Jupyter, etc.)"],"input_types":["SQL SELECT query string"],"output_types":["JSON object containing: query results (rows), Plotly chart JSON, detected chart type"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_5","uri":"capability://memory.knowledge.starrocks.system.information.access.via.proc.style.resources","name":"starrocks system information access via proc-style resources","description":"Exposes StarRocks internal metrics, system state, and performance information through proc:// URI resources (similar to Linux /proc filesystem), allowing AI assistants to query system tables and internal state without direct SQL access. The implementation translates proc:// paths into queries against StarRocks system tables (information_schema, sys database) and caches results to avoid repeated system queries.","intents":["I want an AI assistant to diagnose performance issues by querying system metrics","I need the AI to understand cluster state, node health, and resource utilization","I want to monitor query execution and identify slow queries automatically"],"best_for":["AI-powered database monitoring and alerting systems","Teams building self-optimizing database systems with AI diagnostics","Scenarios where AI assistants need to understand system health for query optimization"],"limitations":["System information access depends on StarRocks version and available system tables","Proc resource paths are not standardized — documentation may be incomplete","Caching system metrics can lead to stale health information in fast-changing environments","No real-time streaming of metrics — only point-in-time snapshots"],"requires":["StarRocks database with READ permissions on information_schema and sys database","StarRocks version with system tables (varies by version)","Network connectivity to StarRocks host"],"input_types":["Proc-style URI paths (proc:///{+path})"],"output_types":["JSON-formatted system information (metrics, state, configuration)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_6","uri":"capability://tool.use.integration.mcp.protocol.translation.and.tool.resource.exposure","name":"mcp protocol translation and tool/resource exposure","description":"Implements the Model Context Protocol (MCP) server specification to expose all StarRocks capabilities (tools and resources) to AI assistants in a standardized, protocol-compliant manner. The implementation handles MCP request/response serialization, tool schema definition, resource URI routing, and error handling according to MCP specification, enabling seamless integration with Claude, ChatGPT, and other MCP-compatible AI platforms.","intents":["I want to use my AI assistant (Claude, ChatGPT) to interact with StarRocks without custom integrations","I need a standardized protocol bridge that works with any MCP-compatible AI platform","I want to expose StarRocks capabilities to AI assistants with proper schema validation and error handling"],"best_for":["Teams integrating StarRocks with Claude, ChatGPT, or other MCP-compatible AI assistants","Organizations building multi-tool AI agent systems with standardized protocol requirements","Developers building AI-powered database applications with protocol-agnostic design"],"limitations":["MCP protocol overhead adds ~50-100ms per request for serialization/deserialization","Tool schema definitions must be manually maintained in sync with implementation changes","Error responses are constrained by MCP protocol format — may lose implementation-specific error details","No built-in authentication beyond environment variable credentials — relies on network isolation"],"requires":["MCP-compatible AI assistant (Claude 3.5+, ChatGPT with MCP support, etc.)","MCP client configuration pointing to MCP server host/port","Python 3.8+ for MCP server runtime","mcp library and dependencies installed"],"input_types":["MCP protocol requests (tool calls, resource reads)"],"output_types":["MCP protocol responses (tool results, resource content, errors)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_7","uri":"capability://automation.workflow.connection.pooling.and.persistent.session.management","name":"connection pooling and persistent session management","description":"Manages a global persistent database connection to StarRocks with automatic reconnection on failure, avoiding connection overhead for rapid-fire queries from AI assistants. The implementation maintains a single connection object at the module level, implements reconnection logic with exponential backoff, and provides connection reset functionality for error recovery without requiring AI assistant awareness of connection state.","intents":["I want rapid query execution without connection setup overhead for each request","I need automatic recovery from transient network failures without AI assistant intervention","I want to minimize latency for multi-turn conversations with frequent database queries"],"best_for":["High-frequency query patterns from AI assistants (10+ queries per minute)","Scenarios where connection setup latency is significant relative to query time","Teams requiring transparent failure recovery without explicit reconnection logic"],"limitations":["Single global connection is not thread-safe — concurrent requests may cause race conditions","Connection state is not persisted across MCP server restarts — requires reconnection","Reconnection logic uses exponential backoff which may delay recovery in fast-changing network conditions","No connection pooling for multiple concurrent queries — serializes requests through single connection"],"requires":["StarRocks database accessible at configured host/port","Valid credentials in STARROCKS_USER and STARROCKS_PASSWORD environment variables","Network connectivity with reasonable timeout values (default 30s)"],"input_types":["Connection parameters (host, port, user, password, database)"],"output_types":["Active database connection object"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-starrocks__cap_8","uri":"capability://automation.workflow.environment.based.configuration.and.credential.management","name":"environment-based configuration and credential management","description":"Configures all StarRocks connection parameters and MCP server settings through environment variables (STARROCKS_HOST, STARROCKS_PORT, STARROCKS_USER, STARROCKS_PASSWORD, STARROCKS_DATABASE, MCP_PORT, etc.), enabling deployment flexibility without code changes. The implementation reads environment variables at server startup and validates required parameters, supporting both local development and containerized/cloud deployments with standard credential injection patterns.","intents":["I want to deploy the MCP server to different environments (dev, staging, prod) without code changes","I need to inject credentials securely via environment variables in containerized deployments","I want to configure MCP server port and other settings for different deployment scenarios"],"best_for":["Teams using containerized deployments (Docker, Kubernetes) with environment variable injection","Organizations with CI/CD pipelines requiring environment-specific configuration","Scenarios requiring credential rotation without code redeployment"],"limitations":["Environment variables are read at startup only — changes require server restart","No support for configuration files or dynamic configuration updates","Credentials are stored in environment variables which may be visible in process listings","No validation of configuration until first query attempt — invalid settings may not be detected at startup"],"requires":["Environment variables set before MCP server startup","Supported environment variables: STARROCKS_HOST, STARROCKS_PORT, STARROCKS_USER, STARROCKS_PASSWORD, STARROCKS_DATABASE, MCP_PORT","Python 3.8+ for environment variable access"],"input_types":["Environment variable strings"],"output_types":["Parsed configuration object"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["StarRocks database instance running and accessible","Network connectivity to StarRocks host (default localhost:9030)","Valid database credentials via STARROCKS_USER and STARROCKS_PASSWORD environment variables","Python 3.8+ for MCP server runtime","StarRocks database with write permissions for the configured user","STARROCKS_USER must have DDL/DML privileges on target tables/databases","Network connectivity to StarRocks host","Python 3.8+ for MCP server","StarRocks database with READ permissions on information_schema","MCP client that supports resource URI resolution"],"failure_modes":["Read-only operations only — cannot modify data or schema with this tool","Result sets are fully materialized in memory before returning — large result sets may cause memory pressure","No built-in pagination or streaming for very large result sets","Query timeout behavior depends on StarRocks server configuration, not client-side limits","No transaction rollback support — failed writes may leave partial state changes","Cache invalidation is table-level only; related views or dependent tables are not automatically refreshed","No dry-run or validation mode — queries execute immediately without preview","DDL operations may lock tables; concurrent read queries could be blocked during schema changes","Resources are read-only — schema modifications must use write_query tool, not resources","Cache is in-memory only; server restart clears all cached metadata","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:21.012Z","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=starrocks","compare_url":"https://unfragile.ai/compare?artifact=starrocks"}},"signature":"zGCfyfVMXV/1Y1gzsL8mq7tvRn6F5838R08sNS7uaYT+lEXylKNhxK1tCdbbutzg7zV+sU9NrZKtQ+RddmqDAg==","signedAt":"2026-06-15T22:35:05.982Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/starrocks","artifact":"https://unfragile.ai/starrocks","verify":"https://unfragile.ai/api/v1/verify?slug=starrocks","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"}}