Rememberizer AI MCP Server for Knowledge Retrieval

Overview

Rememberizer AI MCP Server for Knowledge Retrieval is a lightweight Model Context Protocol (MCP) server that connects model runtimes to Rememberizer-hosted knowledge sources. It acts as a context provider: when a model or orchestration layer needs relevant documents, notes, or memories, it queries the MCP server which translates requests into searches and retrievals against a configured Rememberizer account.

This server is useful when you want low-latency, contextual augmentation for language models without embedding Rememberizer logic inside your model runtime. It centralizes access to Rememberizer data sources, handles authentication, and exposes a stable MCP-compatible endpoint that can be plugged into retrieval-augmented generation (RAG), chat assistants, or any pipeline that needs contextual grounding from Rememberizer content.

Features

• Exposes an MCP-compatible HTTP API to serve contextual documents to model runtimes
• Connects to Rememberizer data sources via API keys and configurable endpoints
• Supports multiple retrieval strategies (keyword, embeddings-backed, hybrid)
• Simple deployment options: Node runtime or Docker container
• Configurable caching and rate-limiting knobs for production usage
• Lightweight, intended to be run alongside your model orchestration layer

Installation / Configuration

Prerequisites: Node.js (16+) or Docker.

Clone and install:

git clone https://github.com/skydeckai/mcp-server-rememberizer.git
cd mcp-server-rememberizer
npm install

Run locally:

# Set environment variables then start
export REMEMBERIZER_API_KEY="your_rememberizer_api_key"
export REMEMBERIZER_BASE_URL="https://api.rememberizer.example"
export PORT=8080

npm start

Run with Docker:

# Build image
docker build -t mcp-server-rememberizer .

# Run container
docker run -e REMEMBERIZER_API_KEY="your_key" \
           -e REMEMBERIZER_BASE_URL="https://api.rememberizer.example" \
           -p 8080:8080 \
           mcp-server-rememberizer

Environment variables (summary):

Configuration file (optional) The server can also accept a JSON config for source mappings and retrieval defaults. Example config snippet:

{
  "sources": [
    { "id": "personal_notes", "type": "notes", "collection": "user_123" },
    { "id": "wiki", "type": "wiki", "collection": "team_docs" }
  ],
  "default": { "source": "personal_notes", "max_results": 5, "strategy": "hybrid" }
}

Pass this file path via an env var or CLI flag (see repository README for exact flag names).

Available Tools / Resources

• GitHub repo: https://github.com/skydeckai/mcp-server-rememberizer
• Rememberizer API (use your provider docs for endpoint specifics)
• MCP specification (follow the Model Context Protocol for integration contract)
• Docker image (build from repo) for containerized deployments

Example usage

Example: basic cURL retrieval request (MCP-style request body):

curl -X POST "http://localhost:8080/mcp/v1/retrieve" \
  -H "Authorization: Bearer <MCP_SERVER_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How do I reset my API key?",
    "context": { "user_id": "user_123" },
    "options": { "source": "personal_notes", "max_results": 3 }
  }'

Response (truncated):

{
  "results": [
    { "id": "note_456", "score": 0.92, "text": "To reset your API key, go to Settings > API Keys..." },
    { "id": "note_789", "score": 0.80, "text": "If you lose your key, revoke and create a new one..." }
  ]
}

Node example (fetch):

import fetch from 'node-fetch';

const res = await fetch('http://localhost:8080/mcp/v1/retrieve', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer <MCP_KEY>' },
  body: JSON.stringify({ query: 'project status for Q2', options: { source: 'wiki' } })
});
const data = await res.json();
console.log(data.results);

Use Cases

• Retrieval-Augmented Generation (RAG): use the MCP server to supply model context during prompt construction. This keeps retrieval logic separate from the model runtime.
• Personalized assistants: fetch user-specific notes and preferences from Rememberizer to ground responses with personal history.
• Knowledge search in apps: serve contextual snippets to power in-app help, support assistants, or internal tooling.
• Multi-source aggregation: combine several Rememberizer collections (notes, wiki, bookmarks) and return ranked results for a single query.

Tips for Production

• Secure the MCP server with network-level controls and API keys; avoid exposing Rememberizer credentials.
• Enable caching (CACHE_TTL) for high-frequency queries to reduce latency and API costs.
• Tune MAX_RESULTS and the retrieval strategy (keyword vs embeddings) according to the model and prompt size limits.
• Monitor latency and error rates; add retry/backoff when calling the Rememberizer API.

For implementation details, CLI flags, and advanced configuration options, see the repository on GitHub: https://github.com/skydeckai/mcp-server-rememberizer.