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Related Servers

Alternatives to DanceFlow

No user-submitted related servers found.

    Related Servers

    • F
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      Serves as a personal MCP hub for development, detecting the current stack and monorepo root, managing Docker Compose actions, querying Context7 documentation, and interacting with Nexus, while also forwarding tools from optional external MCP servers.
      33
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    • A
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      Serves as a single MCP and REST control plane through which AI agents discover, describe, and invoke skills across many live creative applications and DCC sessions, routing one endpoint to multiple running hosts with main-thread-safe dispatch and structured, verified results. It also ships skill discovery, diagnostics, and a CLI/gateway so agents can operate production tools from a single prompt.
      3,588 PyPI
      73
      MIT
    • A
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      Enables MCP-capable AI coding tools to dispatch tasks in parallel to multiple installed AI CLIs with automatic failover, plus run multi-AI discussions and cross-reviews, hand code off between tools, manage shared workspaces, and inspect token usage, traffic, clients, and task memory. Also exposes image recognition (OCR/vision), tool and client administration, and log access through a single local hub.
      2 npm
      MIT
    • A
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      A
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      Enables coding agents to audit and manage installed skills and MCP servers across harnesses, profile startup token overhead, inspect bundled assets, safely remove unused skills, and check for updates via native tools and a local dashboard.
      MIT
    • F
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      Local dashboard and MCP server that maintains a live project registry and skill library, enabling coding agents to pull project context and automatically update status, stack, and next steps.
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    • A
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      Exposes MCP tools that give desktop agents durable two-tier memory with recall and graph traversal, project code intelligence (symbol lookup, impact walking, notes search), and web search with page-to-Markdown conversion. It also runs a local model gateway that routes Anthropic- and OpenAI-shaped traffic across providers with usage accounting.
      MIT

    TDQS

    C2.8/5.0

    Scored across 97 tools

    Disambiguation1/5

    Many tool groups share identical descriptions and overlapping purposes, especially stemlab_doctor/models/analysis_actions/midi_models/evidence_models and framerudiments_renderers/sprites/visualisers. With 97 tools across many components, an agent cannot reliably tell several actions apart without deeper component inspection.

    Naming Consistency4/5

    Names generally follow a predictable snake_case component-prefixed pattern such as danceflow_get_workflow, stemlab_generate_cues, and workflowmoves_wordpress_publish_post. Minor deviations exist where tools are noun-only or use slightly different verb styles, but the convention remains readable.

    Tool Count1/5

    97 tools is far beyond a well-scoped surface and is especially heavy given the number of overlapping inspection and runtime-discovery operations. Many tools appear to be component-specific duplicates rather than distinct high-value actions.

    Completeness4/5

    The surface is broad and includes discovery, references, job management, cancellation, artifacts, releases, and many domain-specific operations across 17 components. There are minor gaps such as no obvious generic workflow executor, but coverage is extensive rather than incomplete.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues