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Simple Memory Extension MCP Server: Extend Context, Track Progress - MCP Implementation

Simple Memory Extension MCP Server: Extend Context, Track Progress

Simple Memory Extension MCP Server: Store coding progress, recall key moments, and track changes seamlessly. Keep your AI's context extended for complex projects.

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Ranked in the top 4% of all AI tools in its category

About Simple Memory Extension MCP Server

What is Simple Memory Extension MCP Server: Extend Context, Track Progress?

Think of this tool as your coding sidekick’s “memory booster.” It’s a server that lets agents (like AI assistants or bots) hold onto longer conversations, track key moments in complex tasks, and recall critical details without getting overwhelmed. Perfect for when you’re knee-deep in a 10-hour coding marathon and need to remember that one obscure variable name from an hour ago.

How to Use Simple Memory Extension MCP Server: Extend Context, Track Progress?

1. Boot it up: Just run npm install && npm start – no wizard required.
2. Store memories: Use commands like store_context_item to save progress snapshots. Example: “Remember this API endpoint setup” → boom, it’s logged.
3. Search smarter: Ask for fuzzy matches with semantic search. Need to find where you fixed a bug? Type “memory leak fix attempt” and let the AI hunt down the closest matches.

Simple Memory Extension MCP Server Features

Key Features of Simple Memory Extension MCP Server: Extend Context, Track Progress?

  • Memory Management Swiss Army Knife: Create namespaces for different projects (e.g., “project_foobar”), delete junk entries, and list everything to avoid clutter.
  • Smart Search Magic: The E5 model turns your vague queries into precise searches. No more “Did I save that in 2023 or 2024?” moments.
  • Hands-Off Automation: Set cursor rules to let the server auto-clean old data or prioritize recent changes – like having a tiny coding elf on retainer.

Use Cases of Simple Memory Extension MCP Server: Extend Context, Track Progress?

Scenario 1: Building a monolithic feature? Store progress checkpoints every 30 minutes to backtrack if things go sideways.
Scenario 2: Collaborating with a team? Use namespaces to share “remember this user story” snippets without clogging Slack.
Scenario 3: Debugging a spaghetti codebase? Tag error logs and solutions to avoid repeating the same mistakes.

Simple Memory Extension MCP Server FAQ

FAQ from Simple Memory Extension MCP Server: Extend Context, Track Progress?

Q: Do I need Python for semantic search?
A: Yep, but the server auto-installs dependencies. Just don’t blame us if your GPU starts hyperventilating.

Q: Can I tweak the memory rules?
A: Absolutely! Edit namespaces like they’re your grocery list. Delete old entries with delete_context_item or nuke entire categories with delete_namespace.

Q: Why the vibe coding disclaimer?
A: Built during caffeine-fueled hacking sessions, not a corporate boardroom. It’s battle-tested but might throw a temper tantrum if you push too hard.

Q: How do I contribute?
A: Fork, code, and submit a PR – we’re all about the “shipping over perfection” ethos here. Even if your PR is just a typo fix, we’ll give you a digital high-five.

Content

Simple Memory Extension MCP Server

An MCP server to extend the context window / memory of agents. Useful when coding big features or vibe coding and need to store/recall progress, key moments or changes or anything worth remembering. Simply ask the agent to store memories and recall whenever you need or ask the agent to fully manage its memory (through cursor rules for example) however it sees fit.

Usage

Starting the Server

npm install
npm start

Available Tools

Context Item Management

  • store_context_item - Store a value with key in namespace
  • retrieve_context_item_by_key - Get value by key
  • delete_context_item - Delete key-value pair

Namespace Management

  • create_namespace - Create new namespace
  • delete_namespace - Delete namespace and all contents
  • list_namespaces - List all namespaces
  • list_context_item_keys - List keys in a namespace

Semantic Search

  • retrieve_context_items_by_semantic_search - Find items by meaning

Semantic Search Implementation

  1. Query converted to vector using E5 model
  2. Text automatically split into chunks for better matching
  3. Cosine similarity calculated between query and stored chunks
  4. Results filtered by threshold and sorted by similarity
  5. Top matches returned with full item values

Development

# Dev server
npm run dev

# Format code
npm run format

.env

# Path to SQLite database file
DB_PATH=./data/context.db

PORT=3000

# Use HTTP SSE or Stdio
USE_HTTP_SSE=true

# Logging Configuration: debug, info, warn, error
LOG_LEVEL=info

Semantic Search

This project includes semantic search capabilities using the E5 embedding model from Hugging Face. This allows you to find context items based on their meaning rather than just exact key matches.

Setup

The semantic search feature requires Python dependencies, but these should be automatically installed when you run: npm run start

Embedding Model

We use the intfloat/multilingual-e5-large-instruct

Notes

Developed mostly while vibe coding, so don't expect much :D. But it works, and I found it helpful so w/e. Feel free to contribute or suggest improvements.

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