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DPF

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Alternatives to DPF

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    Related Servers

    • @matihlabs/mcp by matih-labs

      @matihlabs/mcpofficial

      matih-labs
      A
      license
      Not graded
      quality
      B
      maintenance
      Connects AI clients to the Matih data platform, enabling SQL queries, table profiling, analytics, chart creation, and file uploads through MCP tools with OAuth/PKCE authentication.
      40 npm
      Apache 2.0

    • Settra

      omhq
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      license
      Not graded
      quality
      A
      maintenance
      Turns Google Sheets, CSV, Excel, and Parquet files into durable, queryable data for AI agents. Lets clients securely discover datasets, inspect semantic models, run bounded analytics, and manage approved semantic overlays.
      3
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    • MCP Dataset Onboarding Server

      Magenta91
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables automated dataset processing and onboarding using Google Drive integration. Provides metadata extraction, data quality assessment, and contract generation for CSV/Excel files through natural language interactions.
      1
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    • Data Product MCP

      entropy-data
      A
      license
      A
      quality
      F
      maintenance
      Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.
      4
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    • Switchboard

      TattedLawyer
      A
      license
      Not graded
      quality
      B
      maintenance
      Connects and cleans data from business systems like CRM, billing, and support, then exposes a read-only MCP tool for an AI assistant to generate revenue-risk reports.
      9 npm
      Apache 2.0
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.1/5.0

    Scored across 16 tools

    Disambiguation4/5

    Each tool maps to a distinct workflow step or resource (query, workspace, spec, job, trigger, connection), and the finish_* helpers are clearly paired with their initiating tools. The main ambiguity is between list_data and submit_query, though the descriptions explicitly disambiguate them.

    Naming Consistency4/5

    Tool names are consistently snake_case and mostly verb-first, with clear patterns like finish_* pairing with onboard/update/run. The generic manage_ prefix on manage_connection and manage_trigger is a minor deviation from the more action-specific verbs used elsewhere.

    Tool Count4/5

    At 16 tools, the set is slightly above the typical 3-15 range, but the tools cover multiple distinct workflows including workspaces, specs, jobs, triggers, and connections. The finish_* helpers add count but serve necessary asynchronous continuation steps.

    Completeness4/5

    The tool surface covers the core data integration lifecycle end-to-end: workspace creation, spec onboarding/update/delete, job processing, triggers, scheduled pulls, status polling, and SQL querying. Minor gaps like workspace deletion or job cancellation are not exposed, but call_dpf_api partially offsets this and billing is intentionally left to the web UI.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources