{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-hydrolix","slug":"hydrolix","name":"Hydrolix","type":"mcp","url":"https://github.com/hydrolix/mcp-hydrolix","page_url":"https://unfragile.ai/hydrolix","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-hydrolix__cap_0","uri":"capability://tool.use.integration.time.series.schema.exploration.via.mcp.protocol","name":"time-series schema exploration via mcp protocol","description":"Exposes Hydrolix time-series datalake schema metadata (tables, columns, data types, partitioning) through the Model Context Protocol (MCP), enabling LLM agents to discover and understand available datasets without direct database access. Implements MCP resource and tool handlers that translate Hydrolix catalog APIs into standardized schema introspection endpoints, allowing Claude and other MCP-compatible clients to query table structures, column definitions, and temporal indexing strategies.","intents":["Discover what time-series tables exist in a Hydrolix datalake without writing SQL","Understand column names, types, and partitioning schemes to inform query generation","Explore dataset metadata programmatically from within an LLM agent workflow","Enable Claude to self-serve schema information before constructing analytical queries"],"best_for":["Data engineers building LLM-powered analytics agents","Teams integrating Hydrolix with Claude-based data exploration workflows","Organizations needing schema-aware query generation without exposing raw database access"],"limitations":["Schema discovery is read-only — cannot modify table structures or add columns via MCP","Metadata freshness depends on Hydrolix catalog refresh intervals; real-time schema changes may have latency","No support for cross-datalake schema federation — limited to single Hydrolix instance per MCP server instance"],"requires":["Hydrolix instance with accessible catalog API","MCP-compatible client (Claude, or custom MCP host)","Network connectivity from MCP server to Hydrolix API endpoint","Valid Hydrolix API credentials (token or key-based auth)"],"input_types":["MCP resource requests (schema, table list)","MCP tool invocations (describe table, list columns)"],"output_types":["JSON schema metadata","Structured table/column definitions","Temporal indexing and partitioning information"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hydrolix__cap_1","uri":"capability://code.generation.editing.natural.language.to.hydrolix.sql.query.translation","name":"natural language to hydrolix sql query translation","description":"Translates natural language queries from LLM agents into Hydrolix-compatible SQL, leveraging schema context from the datalake to construct syntactically correct and optimized queries. The MCP server acts as a query builder interface that accepts natural language intent, validates it against discovered schema, and generates executable SQL targeting Hydrolix's columnar time-series engine, including proper time-range filtering and aggregation syntax.","intents":["Ask questions about time-series data in natural language and get back SQL-ready queries","Generate Hydrolix-specific SQL syntax (e.g., time-bucketing, retention policies) from English descriptions","Validate query feasibility against schema before execution to catch errors early","Enable non-SQL-fluent users to query time-series data through LLM intermediaries"],"best_for":["Analytics teams using Claude to generate time-series queries","LLM agents that need to construct Hydrolix queries dynamically","Organizations building natural-language analytics interfaces on top of Hydrolix"],"limitations":["Query generation quality depends on schema clarity and LLM reasoning — complex multi-join queries may require manual refinement","No query optimization hints — generated SQL may not be optimal for large datasets without additional tuning","Limited to SELECT queries; INSERT, UPDATE, DELETE operations not supported via MCP","Temporal query patterns must be explicitly described in natural language; implicit time-range inference not available"],"requires":["Schema exploration capability (prerequisite)","Hydrolix SQL dialect knowledge embedded in LLM context","MCP client capable of multi-turn reasoning (e.g., Claude with tool use)"],"input_types":["Natural language query description","Optional schema hints or table names"],"output_types":["Hydrolix-compatible SQL string","Query validation status","Estimated query complexity metadata"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hydrolix__cap_2","uri":"capability://tool.use.integration.query.execution.and.result.streaming.via.mcp","name":"query execution and result streaming via mcp","description":"Executes validated Hydrolix SQL queries through the MCP protocol and streams results back to LLM agents in structured format (JSON, CSV, or Arrow). The server manages query lifecycle (submission, polling, result pagination) and handles Hydrolix-specific execution semantics like time-range pruning and columnar result formatting, abstracting away connection pooling and error handling from the client.","intents":["Run a generated SQL query against Hydrolix and get results back in the LLM context","Stream large result sets without loading entire dataset into memory","Handle query timeouts and retries transparently within the MCP workflow","Format query results for downstream LLM analysis or visualization"],"best_for":["LLM agents executing exploratory time-series queries","Workflows that need to fetch data and reason over results in a single turn","Teams building interactive analytics dashboards backed by Hydrolix"],"limitations":["Result streaming is limited by MCP message size constraints — very large result sets may require pagination or sampling","Query timeout handling is server-side; client-side cancellation not fully supported","No built-in caching of query results — repeated identical queries re-execute against Hydrolix","Result formatting options limited to JSON and CSV; binary formats (Parquet, Arrow) require additional serialization"],"requires":["Hydrolix query API with async execution support","MCP server with streaming capability","Network connectivity and query permissions on Hydrolix instance"],"input_types":["SQL query string","Optional execution parameters (timeout, result limit)"],"output_types":["Structured result set (JSON rows, CSV)","Query metadata (execution time, row count)","Error messages with diagnostic information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hydrolix__cap_3","uri":"capability://code.generation.editing.time.series.aggregation.and.bucketing.helpers","name":"time-series aggregation and bucketing helpers","description":"Provides MCP tools for common time-series operations (time-bucketing, downsampling, rolling aggregations) that generate Hydrolix-compatible SQL fragments. These helpers encapsulate Hydrolix-specific temporal functions (e.g., DATE_TRUNC, INTERVAL arithmetic) and allow LLM agents to compose complex time-series queries without deep SQL knowledge, automatically handling timezone and precision considerations.","intents":["Bucket time-series data into hourly, daily, or weekly intervals for aggregation","Downsample high-frequency data to lower granularity for visualization","Calculate rolling averages, sums, or other windowed aggregations over time","Handle timezone conversions and daylight-saving-time edge cases automatically"],"best_for":["LLM agents building time-series analytics queries","Non-SQL users who need to perform temporal aggregations","Workflows that require consistent time-bucketing across multiple queries"],"limitations":["Bucketing granularity limited to standard intervals (second, minute, hour, day, week, month); custom intervals require manual SQL","Rolling window functions assume uniform time-series data; sparse or irregular timestamps may produce unexpected results","Timezone handling is best-effort; edge cases around DST transitions may require manual adjustment","No support for calendar-aware bucketing (e.g., fiscal quarters, business days)"],"requires":["Hydrolix instance with temporal function support","Schema context with timestamp column identification"],"input_types":["Table name and timestamp column","Bucketing interval (hour, day, week, etc.)","Aggregation function (sum, avg, count, etc.)"],"output_types":["SQL fragment for time-bucketing","Complete aggregation query template"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hydrolix__cap_4","uri":"capability://data.processing.analysis.multi.table.join.and.correlation.analysis","name":"multi-table join and correlation analysis","description":"Enables LLM agents to discover and construct joins across multiple Hydrolix tables based on schema relationships and common column patterns. The MCP server analyzes table metadata to identify potential join keys (matching column names, types, and temporal alignment) and generates join queries that respect Hydrolix's columnar architecture and time-series semantics, including automatic time-range alignment for correlated datasets.","intents":["Find which tables can be joined based on common columns or time alignment","Generate multi-table queries that correlate metrics from different time-series datasets","Automatically align time-ranges across tables with different granularities","Discover hidden relationships between tables for exploratory analysis"],"best_for":["Data analysts exploring correlations across multiple time-series sources","LLM agents performing root-cause analysis across distributed metrics","Teams building cross-dataset analytics without manual schema mapping"],"limitations":["Join discovery is heuristic-based (column name matching, type compatibility); false positives possible without semantic validation","No support for complex join conditions (e.g., range joins, fuzzy matching); limited to equality joins","Performance implications of multi-table joins not estimated; large joins may timeout without optimization hints","Temporal alignment assumes monotonic timestamps; non-monotonic or sparse data may produce incorrect correlations"],"requires":["Multiple tables in Hydrolix with discoverable join keys","Schema metadata with column type information","Sufficient query timeout for multi-table scans"],"input_types":["List of table names to correlate","Optional join key hints"],"output_types":["Suggested join keys with confidence scores","Multi-table join query template","Time-range alignment metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hydrolix__cap_5","uri":"capability://memory.knowledge.retention.policy.and.data.lifecycle.awareness","name":"retention policy and data lifecycle awareness","description":"Exposes Hydrolix data retention policies and lifecycle metadata through MCP, allowing LLM agents to understand data availability windows and make informed decisions about query time-ranges. The server queries Hydrolix catalog for retention settings, data age, and archival status, enabling agents to warn about stale data or suggest appropriate time-windows for analysis.","intents":["Check if data exists for a requested time-range before generating queries","Understand retention policies to avoid querying deleted or archived data","Recommend appropriate time-windows based on data availability","Alert users when requested analysis falls outside retention window"],"best_for":["LLM agents that need to validate query feasibility before execution","Analytics workflows that must respect data governance and retention policies","Organizations with compliance requirements around data age and availability"],"limitations":["Retention metadata accuracy depends on Hydrolix configuration; misconfigured policies may produce incorrect availability estimates","No real-time visibility into ongoing data ingestion; recent data may not be immediately queryable","Archival status is static snapshot; does not track ongoing archival operations","No support for table-level retention variations; assumes uniform retention across partitions"],"requires":["Hydrolix instance with retention policy configuration","Access to catalog metadata APIs"],"input_types":["Table name","Optional time-range query"],"output_types":["Retention policy details (duration, archival status)","Data availability window (earliest/latest timestamp)","Warnings about stale or unavailable data"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hydrolix__cap_6","uri":"capability://planning.reasoning.performance.metrics.and.query.optimization.hints","name":"performance metrics and query optimization hints","description":"Collects and exposes Hydrolix query performance metrics (execution time, data scanned, partition pruning effectiveness) through MCP, enabling LLM agents to understand query cost and make optimization decisions. The server tracks query performance patterns and suggests optimizations (e.g., narrower time-ranges, pre-aggregation, partition key usage) based on historical execution data and Hydrolix-specific optimization opportunities.","intents":["Understand how expensive a query is before executing it at scale","Get suggestions for optimizing slow or resource-intensive queries","Learn which queries are partition-pruned effectively vs. full-table scans","Make trade-offs between query accuracy and execution cost"],"best_for":["Data engineers optimizing Hydrolix workloads","LLM agents that need to balance query cost and accuracy","Teams building cost-aware analytics workflows"],"limitations":["Performance metrics are historical; estimates for new query patterns may be inaccurate","Optimization suggestions are heuristic-based and may not apply to all workloads","No real-time query cost estimation; cost is only known after execution","Partition pruning effectiveness depends on query structure; not all queries benefit equally"],"requires":["Hydrolix query execution history with performance telemetry","Access to query plan information"],"input_types":["SQL query string","Optional execution context (data volume, time-range)"],"output_types":["Estimated query cost (execution time, data scanned)","Optimization suggestions","Partition pruning analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Hydrolix instance with accessible catalog API","MCP-compatible client (Claude, or custom MCP host)","Network connectivity from MCP server to Hydrolix API endpoint","Valid Hydrolix API credentials (token or key-based auth)","Schema exploration capability (prerequisite)","Hydrolix SQL dialect knowledge embedded in LLM context","MCP client capable of multi-turn reasoning (e.g., Claude with tool use)","Hydrolix query API with async execution support","MCP server with streaming capability","Network connectivity and query permissions on Hydrolix instance"],"failure_modes":["Schema discovery is read-only — cannot modify table structures or add columns via MCP","Metadata freshness depends on Hydrolix catalog refresh intervals; real-time schema changes may have latency","No support for cross-datalake schema federation — limited to single Hydrolix instance per MCP server instance","Query generation quality depends on schema clarity and LLM reasoning — complex multi-join queries may require manual refinement","No query optimization hints — generated SQL may not be optimal for large datasets without additional tuning","Limited to SELECT queries; INSERT, UPDATE, DELETE operations not supported via MCP","Temporal query patterns must be explicitly described in natural language; implicit time-range inference not available","Result streaming is limited by MCP message size constraints — very large result sets may require pagination or sampling","Query timeout handling is server-side; client-side cancellation not fully supported","No built-in caching of query results — repeated identical queries re-execute against Hydrolix","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"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":"2026-06-17T09:51:03.041Z","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=hydrolix","compare_url":"https://unfragile.ai/compare?artifact=hydrolix"}},"signature":"uLqJGwR9JI1pZvDQrD52/eZ2MZ3GbOX8jJzg1DVaiAYncsH7gFpIQNT6edHH7mZNyQu/3nGHnlF3NGuR40imCA==","signedAt":"2026-06-20T18:45:54.862Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hydrolix","artifact":"https://unfragile.ai/hydrolix","verify":"https://unfragile.ai/api/v1/verify?slug=hydrolix","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"}}