{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-keboola","slug":"keboola","name":"Keboola","type":"mcp","url":"https://github.com/keboola/keboola-mcp-server","page_url":"https://unfragile.ai/keboola","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-keboola__cap_0","uri":"capability://tool.use.integration.mcp.based.data.pipeline.orchestration","name":"mcp-based data pipeline orchestration","description":"Exposes Keboola's data workflow engine through the Model Context Protocol (MCP), enabling LLM agents and AI tools to construct, configure, and execute multi-step data pipelines programmatically. Uses MCP's standardized tool-calling interface to abstract Keboola's REST API, allowing agents to compose transformations, extractions, and loads without direct API knowledge.","intents":["I want my AI agent to autonomously build and run data pipelines without hardcoding API calls","I need to let Claude or another LLM orchestrate complex ETL workflows through natural language","I want to integrate Keboola pipeline execution into an agentic workflow without managing credentials directly"],"best_for":["AI engineers building autonomous data engineering agents","Teams using Claude or other MCP-compatible LLMs for data automation","Organizations migrating from REST API orchestration to LLM-driven pipeline management"],"limitations":["MCP protocol overhead adds latency compared to direct REST API calls","LLM context window constraints may limit pipeline complexity that can be described in a single prompt","Requires MCP-compatible client (Claude, custom LLM integration) — not usable with standard REST clients"],"requires":["Keboola account with API token","MCP-compatible LLM client (Claude Desktop, custom MCP host)","Network access to Keboola API endpoints"],"input_types":["natural language instructions from LLM","structured MCP tool parameters (JSON)","pipeline configuration objects"],"output_types":["pipeline execution status","job logs and error messages","structured pipeline metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_1","uri":"capability://planning.reasoning.declarative.pipeline.configuration.through.natural.language","name":"declarative pipeline configuration through natural language","description":"Translates LLM-generated natural language descriptions into Keboola pipeline configurations by mapping intent to pipeline components (extractors, transformations, writers). The MCP server likely implements a schema-aware tool registry that guides LLM generation toward valid Keboola pipeline JSON structures, reducing hallucination and invalid configurations.","intents":["I want to describe a data workflow in English and have it automatically configured in Keboola","I need my AI agent to understand Keboola's component ecosystem and suggest appropriate extractors/transformers","I want to validate pipeline configurations before execution to catch schema errors early"],"best_for":["Non-technical stakeholders who want to define data workflows conversationally","Data engineers using AI copilots to accelerate pipeline development","Teams building self-service data pipeline generation for business users"],"limitations":["LLM may struggle with complex conditional logic or advanced Keboola features not well-represented in training data","Schema validation happens post-generation, so invalid configurations require iterative refinement","Limited to Keboola components and configurations exposed through MCP tool definitions"],"requires":["Keboola account with configured extractors and transformers","MCP server with Keboola pipeline schema definitions","LLM with function-calling capability (Claude, GPT-4, etc.)"],"input_types":["natural language pipeline descriptions","existing pipeline JSON for modification"],"output_types":["Keboola pipeline JSON configuration","validation error messages","pipeline execution plans"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_2","uri":"capability://automation.workflow.real.time.pipeline.execution.monitoring.and.control","name":"real-time pipeline execution monitoring and control","description":"Provides MCP tools for starting, stopping, and monitoring Keboola pipeline jobs with real-time status updates and log streaming. The server polls Keboola's job API and exposes job state, execution metrics, and error logs through MCP's tool interface, enabling agents to react to pipeline events (e.g., retry on failure, escalate on timeout).","intents":["I want my agent to start a pipeline and wait for completion before proceeding","I need to monitor pipeline execution and automatically handle failures or timeouts","I want to stream pipeline logs to my agent for real-time debugging and decision-making"],"best_for":["Autonomous data engineering agents requiring job lifecycle management","Teams building self-healing data pipelines with automatic retry logic","Organizations needing real-time observability of AI-orchestrated data workflows"],"limitations":["Polling-based monitoring introduces latency (typically 5-30 second intervals) compared to webhook-based approaches","Log streaming may be truncated or delayed for very large jobs with verbose output","No built-in persistence of job history — requires external logging system for audit trails"],"requires":["Keboola API token with job management permissions","MCP client capable of handling streaming responses","Network connectivity to Keboola API"],"input_types":["pipeline ID or configuration object","job parameters (runtime variables)","timeout and retry thresholds"],"output_types":["job status (queued, running, success, failed)","execution metrics (duration, rows processed)","log output (text)","error details and stack traces"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_3","uri":"capability://search.retrieval.component.discovery.and.capability.introspection","name":"component discovery and capability introspection","description":"Exposes Keboola's component registry (extractors, transformations, writers) through MCP tools, allowing agents to query available components, their parameters, supported data sources, and transformation capabilities. The server likely caches component metadata and provides search/filter operations to help agents select appropriate components for a given data task.","intents":["I want my agent to know what data sources and transformations are available in our Keboola instance","I need to find the right extractor for a specific data source without manual documentation lookup","I want my agent to suggest components based on data type or transformation requirement"],"best_for":["AI agents building pipelines autonomously without human component selection","Data teams using AI copilots to explore Keboola's component ecosystem","Organizations with large component libraries needing intelligent discovery"],"limitations":["Component metadata may be incomplete or outdated if not synchronized with Keboola regularly","Custom components or third-party integrations may not be discoverable if not registered in Keboola's component registry","No semantic search — discovery is limited to exact name/tag matching unless LLM provides semantic interpretation"],"requires":["Keboola account with component registry access","MCP server with component metadata caching","API token with read permissions on component definitions"],"input_types":["component type filter (extractor, transformation, writer)","data source type (database, API, file)","search keywords or tags"],"output_types":["component metadata (name, description, parameters)","supported data sources and formats","component configuration schema","usage examples or documentation links"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_4","uri":"capability://code.generation.editing.data.transformation.and.sql.execution.within.pipelines","name":"data transformation and sql execution within pipelines","description":"Enables agents to define and execute SQL transformations or Python scripts within Keboola pipelines through MCP tools. The server abstracts Keboola's transformation component APIs, allowing agents to write transformation logic, validate syntax, and execute against staged data without managing compute infrastructure directly.","intents":["I want my agent to write SQL transformations and execute them within a Keboola pipeline","I need to validate transformation logic before committing it to a production pipeline","I want to execute ad-hoc data transformations as part of an agentic workflow"],"best_for":["Data engineers using AI copilots to generate transformation code","Autonomous agents performing data quality checks or enrichment","Teams building self-service analytics with AI-generated transformations"],"limitations":["SQL dialect must match Keboola's transformation backend (Snowflake, BigQuery, etc.) — cross-dialect compatibility not guaranteed","LLM-generated SQL may be inefficient or incorrect, requiring validation and optimization","No built-in data preview or sampling — agents must rely on schema metadata to understand data structure"],"requires":["Keboola transformation component configured (Snowflake, BigQuery, Redshift, etc.)","API token with transformation execution permissions","SQL dialect knowledge matching the configured backend"],"input_types":["SQL transformation code (text)","Python script (for Python-based transformations)","input table references","output table schema"],"output_types":["transformation execution status","row counts and data statistics","error messages and SQL syntax errors","execution logs"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_5","uri":"capability://tool.use.integration.credential.and.configuration.management.for.data.sources","name":"credential and configuration management for data sources","description":"Provides MCP tools for managing connection credentials, API keys, and configuration for Keboola's data sources and extractors. The server likely implements secure credential storage (encrypted at rest) and retrieval through MCP, allowing agents to configure extractors without exposing secrets in agent code or logs.","intents":["I want my agent to configure a new data source connection without hardcoding credentials","I need to securely manage API keys and database passwords for multiple extractors","I want to rotate credentials or update connection parameters without redeploying agent code"],"best_for":["Teams building multi-tenant data platforms with isolated credentials per customer","Organizations with strict credential management policies requiring audit trails","Autonomous agents needing to configure extractors dynamically at runtime"],"limitations":["Credential rotation requires MCP server restart or cache invalidation — no hot-reload capability","No built-in credential validation — agents must test connections after configuration","Secrets are encrypted in Keboola but transmitted over network — requires HTTPS and secure MCP transport"],"requires":["Keboola account with credential management API access","MCP server with secure credential storage (encrypted at rest)","TLS/HTTPS for MCP transport to prevent credential interception"],"input_types":["credential type (API key, database password, OAuth token)","connection parameters (host, port, database name)","data source identifier"],"output_types":["credential storage confirmation","connection test results","credential metadata (creation date, last modified)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_6","uri":"capability://planning.reasoning.data.lineage.and.dependency.tracking","name":"data lineage and dependency tracking","description":"Exposes Keboola's data lineage graph through MCP tools, enabling agents to query data source dependencies, transformation chains, and downstream consumers. The server likely maintains a directed acyclic graph (DAG) of pipeline components and their data flows, allowing agents to understand impact analysis and optimize pipeline execution order.","intents":["I want my agent to understand how data flows through our pipelines and identify bottlenecks","I need to determine the impact of changing a data source or transformation on downstream pipelines","I want to optimize pipeline execution order based on dependency analysis"],"best_for":["Data engineers using AI for pipeline optimization and impact analysis","Organizations with complex, interdependent pipelines needing automated dependency management","Teams building self-healing pipelines that adapt to upstream failures"],"limitations":["Lineage tracking requires explicit pipeline configuration — implicit dependencies (shared tables, external references) may not be captured","DAG construction adds computational overhead for very large pipelines (1000+ components)","No real-time lineage updates — changes to pipeline structure require cache refresh"],"requires":["Keboola pipelines with explicit component dependencies","MCP server with DAG construction and caching","API token with pipeline metadata read access"],"input_types":["pipeline ID or component identifier","lineage depth (how many levels upstream/downstream to traverse)"],"output_types":["dependency graph (JSON or DOT format)","component metadata with execution order","impact analysis (affected components if source changes)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_7","uri":"capability://data.processing.analysis.batch.data.extraction.and.loading.with.format.conversion","name":"batch data extraction and loading with format conversion","description":"Provides MCP tools for extracting data from Keboola storage in multiple formats (CSV, JSON, Parquet) and loading external data into Keboola. The server abstracts Keboola's storage API and file format handling, allowing agents to perform ETL operations without managing file conversions or storage infrastructure directly.","intents":["I want my agent to export pipeline results in a specific format for downstream consumption","I need to load external data files into Keboola without manual upload steps","I want to convert data between formats (CSV to Parquet, JSON to CSV) as part of a workflow"],"best_for":["Agents performing data integration between Keboola and external systems","Teams building automated data export pipelines for reporting or analytics","Organizations needing format-agnostic data movement capabilities"],"limitations":["Large file transfers (>1GB) may timeout or exhaust memory — requires streaming or chunking","Format conversion adds latency — direct storage access is faster than format transformation","No built-in data validation — agents must verify data integrity after loading"],"requires":["Keboola storage access (S3, Azure Blob, or Keboola's managed storage)","API token with storage read/write permissions","Support for target file formats (CSV, JSON, Parquet, etc.)"],"input_types":["table identifier or query","output format (CSV, JSON, Parquet)","compression options (gzip, snappy)","file path or external storage location"],"output_types":["file URL or storage location","file metadata (size, row count, schema)","data preview (first N rows)","format conversion status"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-keboola__cap_8","uri":"capability://automation.workflow.error.handling.and.pipeline.failure.recovery","name":"error handling and pipeline failure recovery","description":"Provides MCP tools for detecting pipeline failures, analyzing error logs, and triggering recovery actions (retry, skip, alert). The server likely implements error classification (transient vs. permanent) and suggests remediation based on error patterns, enabling agents to autonomously handle common failure modes.","intents":["I want my agent to automatically retry failed pipeline steps with exponential backoff","I need to detect data quality issues and trigger alerts or corrective actions","I want my agent to analyze pipeline failures and suggest fixes"],"best_for":["Teams building self-healing, fault-tolerant data pipelines","Autonomous agents requiring robust error handling and recovery logic","Organizations with SLAs requiring automated incident response"],"limitations":["Error classification is heuristic-based — may misidentify transient vs. permanent failures","Recovery suggestions are limited to common patterns — novel failure modes require human intervention","No built-in rollback capability — failed transformations may leave data in inconsistent state"],"requires":["Keboola pipeline with error logging enabled","MCP server with error pattern matching and classification","API token with job management and alerting permissions"],"input_types":["job ID or pipeline identifier","error log output (text)","retry policy (max attempts, backoff strategy)"],"output_types":["error classification (transient, permanent, data quality)","recovery recommendations","alert notifications","remediation action results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Keboola account with API token","MCP-compatible LLM client (Claude Desktop, custom MCP host)","Network access to Keboola API endpoints","Keboola account with configured extractors and transformers","MCP server with Keboola pipeline schema definitions","LLM with function-calling capability (Claude, GPT-4, etc.)","Keboola API token with job management permissions","MCP client capable of handling streaming responses","Network connectivity to Keboola API","Keboola account with component registry access"],"failure_modes":["MCP protocol overhead adds latency compared to direct REST API calls","LLM context window constraints may limit pipeline complexity that can be described in a single prompt","Requires MCP-compatible client (Claude, custom LLM integration) — not usable with standard REST clients","LLM may struggle with complex conditional logic or advanced Keboola features not well-represented in training data","Schema validation happens post-generation, so invalid configurations require iterative refinement","Limited to Keboola components and configurations exposed through MCP tool definitions","Polling-based monitoring introduces latency (typically 5-30 second intervals) compared to webhook-based approaches","Log streaming may be truncated or delayed for very large jobs with verbose output","No built-in persistence of job history — requires external logging system for audit trails","Component metadata may be incomplete or outdated if not synchronized with Keboola regularly","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":"2026-06-17T09:51:03.577Z","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=keboola","compare_url":"https://unfragile.ai/compare?artifact=keboola"}},"signature":"pBjCzvxpx51ntH/okA30WxcqX1oq7UEQcz30sCUzg8qVkdJPMqEKGBzf9EBW/hfTb5et/w99QGa2jncixZ4qCg==","signedAt":"2026-06-19T16:59:53.038Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/keboola","artifact":"https://unfragile.ai/keboola","verify":"https://unfragile.ai/api/v1/verify?slug=keboola","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"}}