Hippocampal Memory MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NEO4J_URI | Yes | The URI for the Neo4j database (e.g., bolt://localhost:7687 or ngrok tunnel URL) | |
| NEO4J_USER | Yes | The username for the Neo4j database | |
| NEO4J_PASSWORD | Yes | The password for the Neo4j database | |
| OPENAI_API_KEY | Yes | Your OpenAI API key for generating embeddings |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| hippocampus_write_eventB | Create an episodic Event with Who/Why/What/Where/Effects blocks. Mimics hippocampal CA3 pattern separation and binding. |
| hippocampus_write_reflectionB | Create agent-relative Reflection about an event. Mimics prefrontal cortex self-referential processing with egocentric perspective. |
| hippocampus_search_eventsA | Retrieve events using pattern completion. Supports semantic search, temporal filters, participant filters, and effect filters. |
| encode_memoryA | Create episodic memory event with emotional valence, temporal context, and entity involvement. Automatically generates embeddings for semantic retrieval. |
| recall_memoryA | Retrieve memories using semantic similarity, emotional valence, temporal range, or entity involvement. Returns ranked results combining vector similarity with Cypher filters. |
| mutate_graphA | Execute arbitrary Cypher write operations for creating/updating nodes, relationships, and properties. Use for operations beyond specialized tools. |
| evolve_bondA | Update bond strength between entities and track relationship trajectory over time. Automatically records milestones and calculates momentum. |
| query_graphA | Execute read-only Cypher queries for complex data retrieval. Use for custom queries, analytics, and exploring the graph structure. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
There is significant overlap between hippocampus_write_event and encode_memory, both creating episodic memory events with similar semantics. Similarly, hippocampus_search_events and recall_memory both retrieve memories with overlapping filters, making it unclear which tool to select. The distinction between generic graph operations (mutate_graph/query_graph) and specialized tools adds further ambiguity.
Naming mixes a hippocampus_ prefix on three tools (write_event, write_reflection, search_events) with plain verb_noun names on the rest (encode_memory, recall_memory, mutate_graph, evolve_bond, query_graph). While the verbs are fairly descriptive, the inconsistent prefix and overlapping vocabulary (write vs encode, search vs recall) break a predictable pattern.
Eight tools is a reasonable count for a memory and knowledge graph server. However, the presence of redundant event creation and retrieval tools means the count is slightly padded; the true functional set is smaller. Still, the overall number is appropriate for the domain.
The server covers creation (events, reflections), retrieval (search, recall), relationship manipulation (evolve_bond), and custom graph operations (mutate_graph, query_graph). However, there are no dedicated tools for updating or deleting memories, and mutating via raw Cypher is an indirect workaround. Notable gaps exist for lifecycle management of memories.