Skip to main content
Glama
auspham

Copilot Memory MCP

by auspham
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to Copilot Memory MCP

No user-submitted related servers found.

    Related Servers

    • Agent Memory

      Masikomoore
      F
      license
      Not graded
      quality
      B
      maintenance
      Provides a self-hosted shared memory service that lets AI agents capture and recall durable facts, decisions, and context across multiple tools and MCP-capable clients.
      3
      -

    • light-agent-memory-mcp-server

      AliYar-Khan
      A
      license
      A
      quality
      B
      maintenance
      Provides persistent, searchable memory for AI agents across any MCP-compatible client, storing project context, user preferences, and session learnings locally in SQLite with tools to save, retrieve, search, and manage them.
      12
      4 npm
      MIT

    • qcdoc-mem

      wychenzhou
      A
      license
      A
      quality
      C
      maintenance
      Provides AI agents with persistent long-term memory over MCP, extracting durable facts from conversations, storing them as plain files, and injecting ranked memories into later sessions via recall and search tools.
      10
      MIT

    • misMEM

      lucasmella-stack
      A
      license
      Not graded
      quality
      A
      maintenance
      Provides persistent, cooperative memory for LLMs via MCP, with SQLite storage and tools for capturing, recalling, consolidating, crystallizing, and forgetting memories across sessions.
      3
      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

    A3.9/5.0

    Scored across 9 tools

    Disambiguation4/5

    The memory tools (recall, save, update, forget, stats) are clearly distinct, and the monitoring tools (monitor_command, run_long_command, watch_file, poll_url) each target a different mechanism. The only potential confusion is between monitor_command and run_long_command, but their descriptions explicitly differentiate repeated polling from single long-running execution.

    Naming Consistency4/5

    Most tools follow a verb_noun convention (save_memory, update_memory, monitor_command, watch_file, poll_url, run_long_command). Minor deviations include recall_memories (plural noun) and memory_stats (noun_noun), which break the strict pattern but remain predictable and readable.

    Tool Count4/5

    At 9 tools, the count is reasonable and not overwhelming. However, the set mixes two unrelated domains (memory persistence and process/URL monitoring), making the scope feel broader than the server name suggests. Still, no tool is redundant and the number is appropriate for its combined purpose.

    Completeness4/5

    The memory lifecycle is complete with recall, save, update, delete, and stats. The monitoring tools cover common asynchronous scenarios: repeated command polling, single long-running command, file watching, and HTTP polling. Minor gaps exist (e.g., no direct tag-based listing, no explicit 'wait' tool), but agents can work around these.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources