{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-hologres","slug":"hologres","name":"Hologres","type":"mcp","url":"https://github.com/aliyun/alibabacloud-hologres-mcp-server","page_url":"https://unfragile.ai/hologres","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-hologres__cap_0","uri":"capability://tool.use.integration.mcp.standardized.sql.query.execution.with.result.streaming","name":"mcp-standardized sql query execution with result streaming","description":"Executes SELECT, DML, and DDL SQL statements against Hologres instances through the Model Context Protocol (MCP) using stdio-based async communication. The server translates AI agent tool invocations into psycopg2 database connections, streams results back as JSON-serialized rows, and handles connection pooling and error propagation through MCP's JSON-RPC message layer. Supports three distinct SQL operation types (SELECT, DML, DDL) as separate callable tools to enable fine-grained permission control and operation categorization.","intents":["Execute analytical queries against Hologres and get structured results back in my AI agent","Run INSERT/UPDATE/DELETE operations on Hologres tables from an LLM without manual SQL construction","Create or modify table schemas in Hologres through agent-driven DDL execution","Integrate Hologres as a data source for multi-step agent reasoning workflows"],"best_for":["AI agents and LLM applications requiring real-time database interaction","Teams building data analytics agents on Alibaba Cloud infrastructure","Developers integrating Hologres as a knowledge base for RAG systems"],"limitations":["Result streaming is row-by-row JSON serialization — large result sets (>100k rows) may cause memory pressure in the MCP server process","No built-in query timeout enforcement — long-running queries can block the stdio transport","DDL operations execute synchronously without transaction rollback capability","Connection pooling is per-server-instance, not per-database — multiple Hologres instances require separate MCP server processes"],"requires":["Hologres instance with network accessibility from MCP server host","Valid Alibaba Cloud credentials (AccessKeyId, AccessKeySecret) or connection string","Python 3.8+","psycopg2 or compatible PostgreSQL driver installed","MCP client (Claude Desktop, custom agent framework) configured to invoke the server"],"input_types":["SQL string (SELECT, INSERT, UPDATE, DELETE, CREATE TABLE, ALTER TABLE, DROP TABLE)","Optional query parameters for parameterized execution"],"output_types":["JSON array of row objects with column names as keys","Error messages with SQL state codes","Row count for DML operations"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_1","uri":"capability://tool.use.integration.serverless.computing.resource.aware.query.execution","name":"serverless computing resource-aware query execution","description":"Executes SELECT queries on Hologres with automatic hg_computing_resource management, allowing agents to specify compute resource allocation (CPU, memory) for individual queries without manual resource provisioning. The server wraps the query execution with SET hg_computing_resource directives before query submission, enabling dynamic resource scaling per query. This is distinct from standard SQL execution because it manages Hologres-specific compute resource hints that control query parallelism and memory allocation.","intents":["Run expensive analytical queries with guaranteed compute resources without blocking other workloads","Let my agent dynamically allocate more CPU/memory to complex queries based on data size","Execute queries with predictable performance characteristics by pre-allocating serverless resources"],"best_for":["Agents performing variable-complexity analytics on Hologres","Multi-tenant systems where query resource isolation is critical","Cost-conscious deployments leveraging Hologres serverless pricing model"],"limitations":["Resource allocation is per-query, not per-session — each query incurs resource setup overhead","No automatic resource estimation — agents must specify resource amounts explicitly or use defaults","Serverless resource pricing is separate from standard Hologres compute — cost tracking requires Alibaba Cloud billing integration","Resource hints are advisory; actual allocation depends on cluster capacity and may be throttled"],"requires":["Hologres instance with serverless computing enabled","Valid hg_computing_resource values (e.g., '4c16g', '8c32g') supported by the Hologres cluster","Appropriate IAM permissions for serverless resource allocation"],"input_types":["SQL SELECT string","Resource specification string (e.g., '4c16g' for 4 cores, 16GB memory)"],"output_types":["JSON array of query results","Resource allocation confirmation in response metadata","Query execution time and resource utilization stats"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_2","uri":"capability://data.processing.analysis.query.plan.analysis.and.optimization.introspection","name":"query plan analysis and optimization introspection","description":"Retrieves and analyzes Hologres query execution plans (EXPLAIN output) and query plans (EXPLAIN PLAN output) to help agents understand query performance characteristics and identify optimization opportunities. The server executes EXPLAIN and EXPLAIN PLAN statements, parses the output into structured format, and exposes plan nodes with estimated costs, cardinality, and execution strategies. This enables agents to reason about query efficiency before execution and suggest rewrites.","intents":["Analyze why a query is slow before running it in production","Get structured query plan data to feed into agent reasoning for query optimization","Understand join strategies and index usage for a given query"],"best_for":["AI agents performing autonomous query optimization","Database performance debugging workflows integrated with LLMs","Educational systems teaching query optimization concepts"],"limitations":["Plan output format varies between Hologres versions — parsing may fail on newer versions without updates","EXPLAIN output is text-based and requires regex/heuristic parsing to extract structured data","Plans are estimates based on table statistics — actual execution may differ significantly","No support for parameterized plan analysis — plans are generated with default parameter assumptions"],"requires":["Hologres instance with EXPLAIN support","Query must be syntactically valid (EXPLAIN will fail on malformed SQL)","Table statistics must be up-to-date for accurate cost estimates"],"input_types":["SQL query string (SELECT, DML, or complex query)"],"output_types":["Structured plan tree with node types, estimated costs, cardinality","Raw EXPLAIN text output","Optimization hints and warnings"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_3","uri":"capability://memory.knowledge.schema.and.table.metadata.introspection.via.uri.resources","name":"schema and table metadata introspection via uri resources","description":"Provides structured access to Hologres database metadata (schemas, tables, columns, DDL, statistics, partitions) through MCP's resource interface using URI patterns like 'hologres:///schemas', 'hologres:///{schema}/tables', and 'hologres:///{schema}/{table}/ddl'. The server maps these URIs to system catalog queries (information_schema, pg_tables, etc.) and returns formatted metadata. This dual-interface approach (tools for operations, resources for metadata) allows agents to browse database structure without executing arbitrary SQL.","intents":["Discover available schemas and tables in a Hologres instance without writing SQL","Retrieve table DDL and column definitions to understand data structure","Get table statistics (row count, size) to inform query planning decisions","Inspect partition information for partitioned tables"],"best_for":["Agents that need to explore database structure before constructing queries","RAG systems building context about available data sources","Data discovery and cataloging workflows"],"limitations":["Resource interface is read-only — no metadata modification through resources","URI patterns are fixed and don't support filtering or complex queries","Statistics may be stale if not recently updated via ANALYZE","Partition information is only available for partitioned tables; non-partitioned tables return empty results"],"requires":["Hologres instance with accessible system catalogs","SELECT permissions on information_schema and system tables"],"input_types":["URI resource path (e.g., 'hologres:///schemas', 'hologres:///public/tables')"],"output_types":["JSON array of schema/table objects with metadata","DDL string for table definitions","Statistics object with row counts and sizes","Partition list with partition names and key ranges"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_4","uri":"capability://tool.use.integration.stored.procedure.invocation.with.parameter.binding","name":"stored procedure invocation with parameter binding","description":"Invokes Hologres stored procedures (PL/pgSQL functions) with parameter binding through the MCP tool interface. The server accepts procedure name, parameter list, and parameter values, constructs a CALL statement with proper type casting, executes it via psycopg2, and returns the procedure result or output parameters. This enables agents to leverage pre-built database logic without constructing complex SQL.","intents":["Call pre-built stored procedures from my agent to perform complex database operations","Execute parameterized procedures with agent-provided values without SQL injection risk","Integrate Hologres business logic (e.g., data validation, aggregation functions) into agent workflows"],"best_for":["Agents integrating with legacy Hologres systems that use stored procedures","Workflows requiring complex database logic encapsulation","Systems where procedure execution is safer than dynamic SQL generation"],"limitations":["Procedure signatures must be known in advance — no dynamic procedure discovery","Parameter type casting is manual — type mismatches cause runtime errors","Output parameters are not fully supported — only return values are captured","Procedure execution is synchronous and blocking — no timeout enforcement"],"requires":["Stored procedure must exist in the Hologres instance","Procedure signature and parameter types must be documented","EXECUTE permission on the procedure"],"input_types":["Procedure name (schema-qualified, e.g., 'public.my_proc')","Parameter array with values and optional type hints"],"output_types":["Procedure return value (scalar or JSON object)","Output parameter values","Execution status and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_5","uri":"capability://tool.use.integration.maxcompute.foreign.table.creation.and.management","name":"maxcompute foreign table creation and management","description":"Creates and manages foreign tables in Hologres that reference MaxCompute (Alibaba's data warehouse) tables, enabling agents to query external data without copying it into Hologres. The server constructs CREATE FOREIGN TABLE statements with MaxCompute-specific options (project, table, partition), executes them, and returns table metadata. This integrates Hologres with the broader Alibaba Cloud data ecosystem.","intents":["Query MaxCompute data directly from Hologres without ETL","Create foreign table definitions for MaxCompute tables to enable agent queries","Join Hologres local tables with MaxCompute external data in a single query"],"best_for":["Agents working in Alibaba Cloud environments with both Hologres and MaxCompute","Data integration workflows that need to combine local and external data","Cost-optimized analytics where some data lives in MaxCompute"],"limitations":["Foreign table creation requires MaxCompute project and table names — no automatic discovery","Query performance on foreign tables is slower than local tables due to network I/O","Partition pruning may not work as expected across the Hologres-MaxCompute boundary","Foreign table schema must be manually specified — no automatic schema inference"],"requires":["Hologres instance with MaxCompute foreign table support enabled","Valid MaxCompute project and table references","Network connectivity between Hologres and MaxCompute","Appropriate IAM permissions for MaxCompute access"],"input_types":["MaxCompute project name","MaxCompute table name","Optional partition specification","Column definitions (name, type)"],"output_types":["Foreign table creation confirmation","Table metadata (columns, types)","Query results from foreign table"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_6","uri":"capability://data.processing.analysis.table.statistics.collection.and.analysis","name":"table statistics collection and analysis","description":"Collects and analyzes table statistics (row counts, column distributions, index usage) in Hologres to support query optimization and cost estimation. The server executes ANALYZE commands on specified tables, retrieves statistics from pg_stat_user_tables and column-level statistics, and formats results for agent consumption. Agents can use these statistics to understand data distribution and inform query planning decisions.","intents":["Gather fresh statistics on a table before running expensive queries","Understand data distribution to help agents estimate query costs","Identify tables with stale statistics that need re-analysis"],"best_for":["Agents performing cost-based query optimization","Automated data quality monitoring workflows","Performance tuning systems that need current statistics"],"limitations":["ANALYZE is a blocking operation — large tables may take minutes to analyze","Statistics are point-in-time snapshots — they become stale as data changes","Column-level statistics may not capture skewed distributions accurately","No support for custom statistics or multi-column correlation analysis"],"requires":["Table must exist in Hologres","ANALYZE permission on the table","Sufficient disk space for statistics storage"],"input_types":["Table name (schema-qualified)","Optional column list for column-specific analysis"],"output_types":["Row count and table size","Column statistics (null count, distinct values, min/max)","Index usage statistics","Last analyze timestamp"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_7","uri":"capability://memory.knowledge.instance.configuration.and.system.monitoring.via.resources","name":"instance configuration and system monitoring via resources","description":"Provides read-only access to Hologres instance configuration, version information, and system activity through MCP resources (URIs like 'system:///hg_instance_version', 'system:///guc_value/{name}', 'system:///query_log/latest/{limit}', 'system:///stat_activity'). The server queries system catalogs and configuration tables, formats results as JSON, and exposes them through the resource interface. This allows agents to understand instance state without executing arbitrary SQL.","intents":["Check Hologres version and instance configuration from my agent","Monitor current query activity and identify long-running queries","Review recent query history to understand workload patterns","Retrieve specific GUC (Grand Unified Configuration) parameters"],"best_for":["Agents performing system health checks and monitoring","Automated performance troubleshooting workflows","Multi-tenant systems needing visibility into instance state"],"limitations":["System resources are read-only — no configuration changes through MCP","Query log and activity data are in-memory snapshots — no historical retention","GUC parameter access is limited to readable parameters — some system parameters may be restricted","Activity monitoring has latency — real-time activity may not be immediately visible"],"requires":["SELECT permissions on system catalogs (pg_stat_activity, pg_stat_statements, etc.)","Access to Hologres configuration tables"],"input_types":["Resource URI (e.g., 'system:///hg_instance_version', 'system:///query_log/latest/100')"],"output_types":["Version string and instance metadata","Configuration parameter values","Query log entries with query text, duration, status","Current query activity with PID, user, query, start time"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hologres__cap_8","uri":"capability://tool.use.integration.mcp.protocol.transport.and.connection.lifecycle.management","name":"mcp protocol transport and connection lifecycle management","description":"Manages the complete MCP server lifecycle including stdio-based transport initialization, JSON-RPC message handling, connection pooling to Hologres, and graceful shutdown. The server uses the mcp Python framework to handle async stdio streams, routes incoming tool/resource requests to appropriate handlers, maintains a persistent psycopg2 connection pool, and implements error handling with proper MCP error responses. This infrastructure enables reliable agent-database communication.","intents":["Establish a stable MCP connection between my AI agent and Hologres","Ensure database connections are pooled and reused efficiently","Handle connection failures and reconnection automatically"],"best_for":["Developers integrating Hologres with MCP-compatible AI agents","Teams deploying Hologres MCP servers in production environments","Systems requiring reliable long-lived agent-database connections"],"limitations":["Connection pooling is in-process — no distributed connection management across multiple MCP server instances","Stdio transport is single-threaded — concurrent requests are queued sequentially","No built-in authentication beyond Hologres credentials — MCP server itself has no access control","Connection timeout and retry logic is hardcoded — no configuration options for tuning"],"requires":["Python 3.8+ with mcp framework installed","MCP client (Claude Desktop, custom agent) configured to invoke the server","Valid Hologres connection credentials (host, port, database, user, password)"],"input_types":["MCP protocol messages (JSON-RPC format)"],"output_types":["MCP protocol responses (JSON-RPC format)","Tool results or resource content","Error responses with error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Hologres instance with network accessibility from MCP server host","Valid Alibaba Cloud credentials (AccessKeyId, AccessKeySecret) or connection string","Python 3.8+","psycopg2 or compatible PostgreSQL driver installed","MCP client (Claude Desktop, custom agent framework) configured to invoke the server","Hologres instance with serverless computing enabled","Valid hg_computing_resource values (e.g., '4c16g', '8c32g') supported by the Hologres cluster","Appropriate IAM permissions for serverless resource allocation","Hologres instance with EXPLAIN support","Query must be syntactically valid (EXPLAIN will fail on malformed SQL)"],"failure_modes":["Result streaming is row-by-row JSON serialization — large result sets (>100k rows) may cause memory pressure in the MCP server process","No built-in query timeout enforcement — long-running queries can block the stdio transport","DDL operations execute synchronously without transaction rollback capability","Connection pooling is per-server-instance, not per-database — multiple Hologres instances require separate MCP server processes","Resource allocation is per-query, not per-session — each query incurs resource setup overhead","No automatic resource estimation — agents must specify resource amounts explicitly or use defaults","Serverless resource pricing is separate from standard Hologres compute — cost tracking requires Alibaba Cloud billing integration","Resource hints are advisory; actual allocation depends on cluster capacity and may be throttled","Plan output format varies between Hologres versions — parsing may fail on newer versions without updates","EXPLAIN output is text-based and requires regex/heuristic parsing to extract structured data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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":"2026-05-24T12:16:20.441Z","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=hologres","compare_url":"https://unfragile.ai/compare?artifact=hologres"}},"signature":"xPt4L9gO2wUw8OsIDpPofJy3eswDYoKTXZfW+BbJpjAEFqMFn++eGfmyMgozpwCO/SxX42p5avCb65UX3RTaAQ==","signedAt":"2026-06-17T03:44:26.377Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hologres","artifact":"https://unfragile.ai/hologres","verify":"https://unfragile.ai/api/v1/verify?slug=hologres","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"}}