Skip to main content
Glama
bejaouibechir

hydra-etl

by bejaouibechir
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Related Servers

Alternatives to hydra-etl

No user-submitted related servers found.

    Related Servers

    • data-detective

      themroats
      A
      license
      Not graded
      quality
      D
      maintenance
      An MCP server that gives AI assistants the ability to connect to, query, profile, and monitor data sources — turning any LLM into an interactive data engineering copilot.
      MIT

    • Datris MCP Server

      datris
      A
      license
      Not graded
      quality
      B
      maintenance
      MCP server with 32 tools for ETL ingestion, AI-generated data quality rules, AI transformations, vector search, and natural-language SQL. Works across Postgres, MongoDB, Kafka, S3/MinIO, HashiCorp Vault, and five vector stores (Qdrant, Weaviate, Milvus, Chroma, pgvector).
      12
      AGPL 3.0

    • mcp-data-profiler

      Ridadata
      A
      license
      A
      quality
      A
      maintenance
      MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.
      1
      29 PyPI
      1
      MIT
    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A4.1/5.0

    Scored across 17 tools

    Disambiguation5/5

    Each tool targets a distinct resource and action: metadata discovery, job lifecycle, workflow lifecycle, data preview, and error explanation. The only near-overlap, validate_job vs check_job, is explicitly differentiated in the descriptions.

    Naming Consistency5/5

    All tools share the hydra_ prefix and follow a verb_noun pattern (list_, describe_, read_, validate_, write_, run_, explain_, preview_, check_, find_). Verbs and nouns are singular/plural only when logically appropriate.

    Tool Count4/5

    17 tools is slightly above the ideal 3-15 range, but each tool earns its place across job authoring, workflow orchestration, validation, execution, and discovery. The count feels a little heavy but not bloated.

    Completeness4/5

    The surface covers the full job and workflow authoring lifecycle: discover, describe, read, validate, write, run, check, and preview. Minor gaps exist: there is no delete or cancel tool for jobs/workflows, and workflow validation is only mentioned as a precondition of write rather than exposed as a separate tool.

    Maintenance

    ActivityMaintained
    ResponsivenessUnresponsive

    Resources