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    • A
      license
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      quality
      A
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      Deploys and serves Haystack Pipelines and Agents as REST APIs and MCP tools, enabling AI assistants to interact with custom NLP pipelines for tasks like RAG, document processing, and intelligent agents through natural language.
      5,210 PyPI
      154
      Apache 2.0
    • F
      license
      A
      quality
      B
      maintenance
      Enables Claude to inspect, configure, run, and modify individual RAG/agent pipeline stages (retrieve, rerank, generate, eval) as structured typed tool calls, and execute the full pipeline end to end.
      5
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    • F
      license
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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables running durable, traceable AI agents via LangGraph through a universal MCP interface, integrating with Hatchet for orchestration, logging, and retries. Provides tools for knowledge management (ingestion, RAG) and Kubernetes operations (diagnosis, auto-fix).
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    TDQS

    B3.3/5.0

    Scored across 83 tools

    Disambiguation4/5

    Most tools target a clearly distinct resource+action, and the descriptions carefully distinguish subtle pairs (get_pipeline_logs vs get_pipeline_trace_logs, activate_deployment vs activate_deployment_revision). The main overlap is the search trio (search_pipeline, search_pipeline_with_params, search_pipeline_with_filters), where with_params also accepts filters, plus list_search_history vs list_pipeline_search_history and list_pipeline_traces overlapping in scope.

    Naming Consistency4/5

    The set overwhelmingly follows a consistent verb_noun snake_case convention (list_, get_, create_, update_, delete_, search_, validate_, deploy_). Minor deviations exist in a handful of noun/tool-first names (yq_object_store, sed_object_store, grep_object_store, load_skill), but these are readable and don't undermine predictability.

    Tool Count2/5

    83 tools is very heavy and exceeds the range where an agent can reliably select without help, even given the genuinely broad platform scope. Many fine-grained operations (per-revision, per-tag, per-cost-view) could be consolidated, making the surface hard to navigate.

    Completeness3/5

    Read/write coverage is broad across pipelines, deployments, indexes, workspaces, evaluations, costs, and docs, but delete/lifecycle operations are missing for several core resources (no delete_pipeline, delete_index, update/delete_workspace, and no create_secret despite get/list_secret). These gaps will cause dead ends for full CRUD workflows.

    Maintenance

    ActivityActive
    ResponsivenessNo issues