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Related Servers

Alternatives to agent-team-lab

No user-submitted related servers found.

    Related Servers

    • A
      license
      C
      quality
      C
      maintenance
      Enables coding agents to coordinate around shared project context, durable tasks, messages, artifacts, multimodal analysis requests, design-system checks, change proposals, and review requests through a compact MCP interface. It persists state locally in JSON and exposes tools, resources, and prompts for agent-to-agent workflows.
      9
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      A reusable AI software development team built on MCP. 13 specialized agents (Project Manager, Backend, Frontend, QA, Security, DevOps, UX, and more) collaborate via shared SQLite state. Exposes 44 MCP tools across 12 domains. All discussions and decisions are stored in the database so agents get back up to speed immediately when re-loaded.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding providers to run structured multi-agent workflows with document-backed specifications, tracked tasks, handoffs, health gates, and completion evidence. It exposes MCP tools for coordinating use cases, specs, implementation, review, and repair across supported providers.
      29 npm
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables delegating tasks to agents you configure yourself through OpenAI-compatible Chat Completions backends, with automatic or explicit routing, synchronous calls, persistent asynchronous jobs, and multi-step reviewed workflows. It returns proposals and analyses only, leaving file changes, command execution, and approvals under the MCP client's control.
      29 npm
      MIT

    TDQS

    B3.4/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clear, distinct purpose: listing expert roles, consulting a single expert, and running the full team workflow. There is no ambiguity or overlap between them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case: list_experts, ask_expert, run_team. The naming is uniform and predictable.

    Tool Count5/5

    Three tools is a well-scoped set for an agent team lab. Each tool addresses a core interaction—discover, consult, execute—without unnecessary bloat.

    Completeness4/5

    The core workflows are covered: listing experts, asking one expert, and running the full team. Minor gaps exist such as no explicit status/result polling or workflow cancellation, but the surface is reasonably complete for its scope.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues