Hystersis MCP Server
Click on "Deploy 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., "@Hystersis MCP Server Save this code snippet to my project memory."
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.
Hystersis MCP Server acts as the universal bridge connecting your local or cloud-hosted Hystersis backend to any MCP-compatible AI assistant (e.g., Claude Desktop, Cursor, Windsurf, Cline).
By attaching this server to your coding assistant, your AI instantly gains long-term, persistent memory, a Neo4j knowledge graph, and ProMem-style extraction across all your chats and workspaces.
✨ Features
Long-Term Context: AI assistants remember coding preferences, architecture rules, and past bugs across completely different chats and workspaces.
Graph Knowledge Base: Exposes tools for the AI to explicitly create entities and relationships, building an architectural map of your systems as it works.
Multi-Hop Spreading Activation: Search through memories not just by vector similarity, but through associative graph propagation (Neo4j + Qdrant).
Plug-and-Play: Installs globally via NPM and connects to Claude or Cursor with three lines of JSON.
Related MCP server: Neo4j Memory Server
🚀 Quick Start
1. Prerequisites
Ensure your core Hystersis engine is running. You can start it locally via Docker:
git clone https://github.com/Himan-D/agent-memory.git
cd agent-memory
docker-compose up -d(By default, the Hystersis API runs on http://localhost:8080)
2. Client Integrations
You don't need to clone this repository to use the server. You can execute it directly via npx hystersis-mcp in your assistant's configuration.
🤖 Claude Desktop
Add the following to your claude_desktop_config.json (Mac: ~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"hystersis": {
"command": "npx",
"args": [
"-y",
"hystersis-mcp"
],
"env": {
"HYSTERSIS_API_URL": "http://localhost:8080",
"HYSTERSIS_API_KEY": "default-key"
}
}
}
}💻 Cursor IDE
Go to Cursor Settings > Features > MCP.
Click + Add new MCP server.
Set the name to
Hystersis.Set the type to
command.Set the command to:
npx -y hystersis-mcp(If your API requires a specific key or URL, you may need to export it in your environment or wrap the command in a bash script).
🛠 Cline / RooCode (VS Code Extensions)
Add to your MCP settings file:
{
"mcpServers": {
"hystersis": {
"command": "npx",
"args": ["-y", "hystersis-mcp"],
"env": {
"HYSTERSIS_API_URL": "http://localhost:8080"
}
}
}
}🧰 Available Tools (Exposed to AI)
When connected, the AI assistant automatically discovers and can independently utilize the following tools:
Tool | Description |
| Ingests a new fact, rule, or preference into the persistent database. |
| Retrieves historical context. Supports |
| Creates a strict named node in the Neo4j knowledge graph. |
| Links two entities with a specific relationship type (e.g., |
| Fetches the aggregated agent state and active working memory. |
| Retrieves real-time telemetry on the ProMem engine's token reduction. |
🛠 Local Development
If you wish to modify the MCP server or contribute:
Clone & Install
git clone https://github.com/Himan-D/hystersis-mcp.git cd hystersis-mcp npm installBuild
npm run buildTest the MCP Output Manually
echo '{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}' | npm start
📝 License
This project is licensed under the MIT License.
Available Tools
6 toolsadd_memoryA
Add a new persistent memory or fact to Hystersis. Use this when the user shares a preference, fact, or instruction that should be remembered across sessions.
Inputs: content (required), user_id, category.
compression_statsA
Retrieve compression metrics and token savings from the Hystersis ProMem engine.
Inputs: no named parameters.
create_entityC
Create a named entity in the Hystersis knowledge graph.
Inputs: name (required), type (required), properties.
create_relationB
Create a relationship between two entities in the Hystersis knowledge graph.
Inputs: from_entity (required), to_entity (required), relation_type (required).
get_contextA
Retrieve aggregated agent context from Hystersis including relevant memories for the current state.
Inputs: agent_id (required).
search_memoriesC
Search for existing memories in Hystersis using semantic or graph spreading activation.
Inputs: query (required), mode.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 6 tools
Each tool has a distinct purpose: adding memories, searching, retrieving context, creating entities/relations, and getting stats. No overlap.
All tool names follow a consistent verb_noun snake_case pattern (e.g., add_memory, create_entity, get_context).
With 6 tools, the server is well-scoped, covering core memory and knowledge graph operations without being overly sparse or bloated.
Missing update/delete operations for memories and entities, and no read entity/relation tools, creating gaps in lifecycle coverage.
Maintenance
Related MCP Connectors
Gives your AI assistant persistent memory and intelligence about your work patterns.
Persistent memory for AI assistants: store, search, and connect knowledge across conversations.
Shared long-term memory for AI agents: save and recall context as a searchable knowledge graph.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
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