Loxo
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LoxoStore: 'Project deadline is June 30th' as knowledge."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Enzan
(name is still in progress) this is fomalization of Cognition into an available name / domain
Typed, structured, self-maintaining memory for AI agents.
Named for 演算 (enzan) — Japanese for computation. Also 遠山 — the distant mountain you can only see when you have enough memory to look back far.
Most AI memory products are flat vector stores. Enzan is different: a typed, curated, relationship-aware knowledge layer with confidence tracking, provenance, pattern recognition, and maintenance semantics built in. Your agents don't just retrieve — they reason over a cortex that gets sharper over time.
Related MCP server: Memsolus MCP Server
What makes Enzan different
Capability | Flat vector stores | Enzan |
Typed documents ( | — | ✓ |
Confidence + provenance tracking | — | ✓ |
Pattern signals with counter-examples | — | ✓ |
Supersession / conflict detection | — | ✓ |
Blindspot analysis | — | ✓ |
Self-maintaining (lint, stale detection) | — | ✓ |
Multi-tenant, MCP-native | — | ✓ |
Document types
knowledge— facts, claims, concepts with confidence, source strength, and optional expiryskill— reusable techniques with steps, pitfalls, and source attributionpattern— recurring structures recognizable fromsignals[], with examples and counter-examplesquestion— logged user queries for blindspot analysis
MCP tools
Connect via any MCP-compatible client (Claude, Cursor, Windsurf, OpenClaw, etc.):
Tool | Description |
| Semantic + keyword search across your cortex |
| Upsert a typed knowledge doc with confidence + provenance |
| Upsert a reusable skill doc |
| Upsert a pattern with signals and domain |
| Append/dedupe an example on an existing pattern |
| Record a user question for blindspot analysis |
| Analyze your question corpus against external cognitive frames |
| Generic escape hatch for arbitrary cortex docs |
Quickstart
# Install the Enzan MCP server
npx @sparksharе-io/enzan
# Or add to your MCP config manually:
{
"mcpServers": {
"enzan": {
"command": "npx",
"args": ["@sparksharе-io/enzan"],
"env": {
"ENZAN_API_KEY": "ez_your_key_here"
}
}
}
}Get your API key at enzan.ai — free tier available.
Architecture
AI Agent (Claude, GPT, etc.)
↓ MCP over HTTP/SSE
Enzan Gateway
↓ API key → tenant namespace
Azure Cosmos DB (per-tenant container)
↓
Azure OpenAI (embeddings)Self-hosted
Enzan runs on any Node.js host with a Cosmos DB backend.
git clone https://github.com/SparkShare-io/enzan
cd enzan
cp .env.example .env # fill in your Cosmos + Azure OpenAI credentials
npm install
npm startRoadmap
See ROADMAP.md.
License
MIT — SparkShare.io
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Flicense-qualityFmaintenanceProvides AI agents with persistent memory and knowledge management through a comprehensive knowledge graph platform. Enables storing, searching, and managing entities, relationships, and observations with advanced features like trending analysis and smart ranking.Last updated3

Memsolus MCP Serverofficial
AlicenseAqualityDmaintenanceProvides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.Last updated1411MIT- Alicense-qualityDmaintenanceEnables AI agents with persistent semantic memory, including semantic recall, knowledge graphs, and instant domain expertise via pre-built Intelligence Packs.Last updated59MIT
- Alicense-qualityDmaintenanceEnables AI agents to store, search, and recall semantic memories with three memory types (semantic, episodic, procedural) and auto-consolidation, compounding intelligence over time.Last updated16MIT
Related MCP Connectors
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Curated knowledge API for AI agents - skill packs, semantic search, validated patterns.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/SparkShare-io/loxo'
If you have feedback or need assistance with the MCP directory API, please join our Discord server