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MCP-Server-Memory: Seamless Failover, Zero Data Loss - MCP Implementation

MCP-Server-Memory: Seamless Failover, Zero Data Loss

MCP-Server-Memory ensures seamless failover and zero data loss with mirrored modules, delivering enterprise-grade reliability and optimized performance under pressure.

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About MCP-Server-Memory

What is MCP-Server-Memory: Seamless Failover, Zero Data Loss?

MCP-Server-Memory is a tool designed to streamline contextual memory management for LLMs like Claude. It operates as a lightweight text-based repository where chat interactions are stored as discrete entries. By treating memories as simple strings in a text file, it ensures robustness against failures while maintaining accessibility. This approach mirrors ChatGPT’s memory handling but with explicit control via API-like commands, guaranteeing that no data is lost even during abrupt interruptions.

How to use MCP-Server-Memory: Seamless Failover, Zero Data Loss?

Interactions revolve around four core actions: memory_add() to log new entries, memory_search() to retrieve matches, memory_delete() to purge outdated info, and memory_list() to audit the full log. For instance, correcting a name involves searching for the old entry, deleting it, then adding the updated version. Crucially, the system auto-includes context from both user and LLM actions—like noting tool failures (e.g., “python not present, use python3”) to refine future responses.

MCP-Server-Memory Features

Key Features of MCP-Server-Memory: Seamless Failover, Zero Data Loss?

At its core, the tool prioritizes durability through text-file storage, ensuring persistence even during crashes. The seamless failover is achieved by maintaining an immutable audit trail, while zero data loss is enforced via atomic write operations. Advanced features include contextual cueing—LLMs can reference prior entries to resolve ambiguities—and dynamic updates via the memory_add()/delete() combo. The minimalist design also allows easy integration with existing workflows without requiring database overhead.

Use Cases for MCP-Server-Memory: Seamless Failover, Zero Data Loss?

MCP-Server-Memory FAQ

FAQ: MCP-Server-Memory’s Nuances

Q: How does it prevent data loss during network hiccups?
A: Memory writes are atomic and sync immediately to disk, ensuring partial writes never corrupt the log.

Q: Can I prioritize certain memories?
A: Not natively, but appending timestamps lets you sort entries manually or via custom scripts.

Q: Does it auto-detect when to log interactions?
A: No—it’s designed for explicit control. Developers must trigger commands based on context, ensuring intentional data retention.

Content

mcp-server-memory

This is an MCP server to interact with a memory text file to help Claude with inter-chat context.

Each line is a memory.

These tools allow Claude (and other MCP clients) to manage memories mid-chat:

  • memory_add(memory: string) - append the memory
  • memory_search(query: string) - return matching memories (substring exact match) - later, might allow globs/regex
  • memory_delete(query: string) - delete matching memories (substring exact match)
  • memory_list() - return all memories
  • FYI memory_update == memory_delete + memory_add

For example,

  • I mention my name => "talking to Wes"
  • metion daughter's age => "Wes's daughter is 8"
  • say working on a typescript project => "working on typescript project"
  • AND, this is critical, can be based on things Claude (assistant/LLM) says or does...
    • Notably, tool use (i.e. run_command)... say there is a failure on a first attempt to use the tool (i.e. the python command isn't present) and then a subsequent tool use succeeds (i.e. using python3 instead of python) => Claude can record "use python3, python is not present"...
  • I ask Claude to get rid of memories about X => memory_delete(query: X)
  • I correct my name => memory_search("oldname") + memory_delete(each matching record, or a common subset query) + memory_add("newname")

Then, when a new chat begins, Claude will automatically get recent memories (a subset or all) OR can ask for memories (some/more/all). And then can use those to influence responses/tools/etc.

Design

A simple memory text file, why:

  • ChatGPT's memory works well and is essentially a text file
    • Maybe it's structured behind the scenes, however if you review your memory its presented as a text file.
  • My testing of a similar reminders feature for mcp-server-commands worked great (when Claude had them).
  • Unstructured text simplifies the tooling and parameters to basically managing a list of strings.

Cueing mechanism:

  • It's also important to have a cue for the model to know when to store memories. This is a bit more unclear how best to do this but..
  • Training: OpenAI acknowledges some training of models to know when to store memories. Just like models are trained for tool use.
  • Prompt: A system prompt component likely contains a reminder to trigger storing memories.
  • Tool alone: In my testing of Claude, with a tool spec alone, and even with hints/suggestions in tool responses, I couldn't get Claude to store memories. So this alone is not sufficient. Seems like Claude's training with tools is to only use them in pursuit of the prompt/request and thus why I believe adding a reminder/cue in a prompt component will work well.

TODOs/Ideas

I have no idea if these are worth the time, just listing ideas here for the future. Perhaps in part to stop myself from working on them :)

  • Recency factor: a way to rearrange memories based on recency?
    • Order then becomes relevant for ambiguous memory queries (i.e. work on typescript project and python project then I ask to start a new project, could suggest the most recently used one?)
  • Fade out old memories?

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