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.
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
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)
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.
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"
}
}
}
}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).
Add to your MCP settings file:
{
"mcpServers": {
"hystersis": {
"command": "npx",
"args": ["-y", "hystersis-mcp"],
"env": {
"HYSTERSIS_API_URL": "http://localhost:8080"
}
}
}
}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. |
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 startThis project is licensed under the MIT License.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The content of the memory or fact | |
| user_id | No | Optional user ID to associate the memory with | |
| category | No | Category of the memory (e.g., preference, fact, code_style) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits but only mentions 'persistent.' It lacks information on side effects (e.g., overwrite behavior, idempotency), error handling, permissions, or rate limits. This is insufficient for a write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, consisting of two short sentences with no waste. The action and usage guideline are front-loaded, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 parameters, no output schema, no annotations), the description covers purpose and usage adequately but omits what the tool returns or confirms success. More context on the response would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage with clear descriptions for all three parameters. The tool description adds no additional parameter semantics, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Add') and resource ('persistent memory or fact') to clearly state the tool's purpose. It distinguishes from sibling tools like create_entity and search_memories, as adding memory is a distinct action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides an explicit usage scenario: 'Use this when the user shares a preference, fact, or instruction that should be remembered across sessions.' It does not include exclusions or alternatives, but the context is clear enough for typical use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve compression metrics and token savings from the Hystersis ProMem engine.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a safe read operation ('Retrieve'), but with no annotations, it carries the full burden. It does not disclose any potential side effects, rate limits, or dependencies beyond the basic retrieval nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the key action and object. Every word contributes meaning, with no redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and a straightforward retrieval purpose, the description provides sufficient context about the output (compression metrics and token savings). It lacks mention of whether it requires prior actions or if the data is real-time, but it is adequate for a simple stat tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description does not need to add parameter information. Schema coverage is 100%, and the description appropriately focuses on what the tool returns rather than parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves compression metrics and token savings from a specific engine. It distinguishes itself from siblings like add_memory and create_entity, which are about memory management operations, by focusing on statistical retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. While the purpose is clear, the description does not mention prerequisites, context, or exclusions. Usage is implied but not elaborated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a named entity in the Hystersis knowledge graph.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Name of the entity (e.g., 'React', 'Alice') | |
| type | Yes | Type of the entity (e.g., 'Framework', 'Person') | |
| properties | No | Additional key-value properties |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only indicates 'create' without detailing side effects, permissions, or return behavior. For a creation tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence. It could include more detail but remains concise and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Missing output schema, behavioral details, and handling of nested objects. The description is too brief for a creation tool with multiple parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description does not add extra meaning beyond the schema. Baseline at 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool creates a named entity in the Hystersis knowledge graph, with a clear verb and resource. However, it does not distinguish from siblings like 'create_relation' or 'add_memory'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided; the description does not indicate when to use this tool versus alternatives or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a relationship between two entities in the Hystersis knowledge graph.
| Name | Required | Description | Default |
|---|---|---|---|
| from_entity | Yes | Source entity ID or name | |
| to_entity | Yes | Target entity ID or name | |
| relation_type | Yes | Relationship type (e.g., 'USES', 'KNOWS', 'DEPENDS_ON') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose side effects, idempotency, permissions, or error cases (e.g., duplicate relationship). Mutation tool needs more behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no extraneous words, clearly communicates the tool's core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple create operation with clear parameters, but lacks usage guidelines and behavioral notes. Could be improved with idempotency or prerequisite info.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all 3 parameters with descriptions (source entity, target entity, relation type). Description adds no extra param info beyond schema. Baseline 3 as schema coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Create', resource 'relationship between two entities', and context 'Hystersis knowledge graph'. Differentiates from sibling create_entity which creates entities, not relationships.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. Implies usage for connecting entities, but no exclusions or when-not-to-use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve aggregated agent context from Hystersis including relevant memories for the current state.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | Agent identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It implies read-only retrieval but doesn't explicitly state that the tool is non-destructive or require special permissions. Minimal context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no wasted words, fully front-loading the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description is sufficient to understand the tool's basic function. However, it could elaborate on what 'aggregated agent context' includes to improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'agent_id', and the tool description adds no additional meaning beyond the schema's 'Agent identifier' label.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('retrieve') and resource ('aggregated agent context'), clearly distinguishing it from sibling tools like 'search_memories' which focus on searching rather than aggregation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool does but provides no guidance on when to use it versus alternatives like 'search_memories', nor any prerequisites or context for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for existing memories in Hystersis using semantic or graph spreading activation.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query | |
| mode | No | Search mode: semantic (default vector), hybrid (vector+keyword), spreading (graph multi-hop) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavioral traits. It only mentions search modes but does not explain what the tool returns, whether it has side effects, performance implications, or authentication needs. The description is insufficient for an AI agent to understand the tool's full behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence (12 words) that communicates the core purpose. It is appropriately front-loaded with the verb 'Search.' However, it could be slightly more informative without becoming verbose, hence a 4 rather than a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (two parameters, no output schema, no annotations) and the presence of sibling tools, the description is incomplete. It does not clarify what the output looks like, how to interpret results, or how this tool fits among siblings like get_context. An AI agent would lack key context to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents both parameters adequately. The description adds no additional meaning beyond what is in the schema. Per guidelines, baseline is 3 when coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: searching for existing memories. It mentions the specific techniques (semantic or graph spreading activation), which helps clarify the scope. However, it does not differentiate this tool from siblings like get_context, which might also involve retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. The description simply says 'search for existing memories,' which implies its use case, but fails to address when to choose this over get_context or other search-like operations. No exclusion criteria or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0add_memorycompression_statscreate_entitycreate_relationget_contextsearch_memoriesScored 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.
Gives your AI assistant persistent memory and intelligence about your work patterns.
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.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
