Skip to main content
Glama
MrMirhan

Task Runner

by MrMirhan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Related Servers

Alternatives to Task Runner

No user-submitted related servers found.

    Related Servers

    • Sugar

      Ploy28598
      F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to read and write persistent project-scoped and cross-project memory during sessions, using local semantic search to surface relevant decisions, preferences, error patterns, and guidelines across sessions. It also optionally exposes an autonomous task queue that draws on that same stored context.
      -

    • ai-cortex

      ai-creed
      A
      license
      Not graded
      quality
      B
      maintenance
      Provides durable project context for coding agents, including project maps, session history, and explicit memories, all stored locally.
      23 npm
      7
      MIT
    • RyeDB MCP Server by RyeDB

      RyeDB MCP Serverofficial

      RyeDB
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables coding agents to keep persistent project memory—rules, decisions, known failures and fixes—and to exchange durable agent-to-agent messages over a channel, including tasks, results, and acknowledgements.
      Apache 2.0

    • engram

      coinstax
      A
      license
      Not graded
      quality
      C
      maintenance
      Provides persistent project memory for AI coding agents, enabling context retention across sessions via event logging, briefing generation, and querying.
      MIT
    Deploy Server
    Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

    TDQS

    B3.4/5.0

    Scored across 19 tools

    Disambiguation3/5

    The memories cluster (save_memory / remember / correct_fact / recall / get_context / what_do_i_know) has real boundary overlap, though most descriptions explicitly cross-reference each other to steer selection. The pair 'rules' vs 'taste' is the weakest split: both are standing user preferences injected at session start, differing only in approval flow. Task tools themselves are clearly distinct.

    Naming Consistency3/5

    Most names are snake_case verb_noun (create_task_group, get_next_task, complete_task, clear_tasks, correct_fact), which is readable and mostly predictable. But several are bare nouns (rules, taste, worklog, graph_stats) and one is a question phrase (what_do_i_know), so the pattern breaks down noticeably.

    Tool Count3/5

    19 tools is at the heavy end for a server named 'Task Runner' whose actual task surface is only four tools (create_task_group, get_next_task, complete_task, get_task_status). The remainder spans memory/knowledge graph, rules, taste, and worklog, so the count feels large relative to the advertised scope.

    Completeness4/5

    Memory has near-full lifecycle (remember, link, recall, what_do_i_know, correct_fact, forget, graph_stats) and the task loop (create, next, complete, status, clear) is workable. Gaps remain: no way to update/reopen/delete a single task, no note edit or delete, and rules support only show/propose with no withdraw path. Agents can work around these, so it stays above mid.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources