How do I use memory-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@memory-mcp Remember that I prefer dark mode" 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.

memory-mcp

by lakehouse-42

Overview Schema Source Related Servers Discussions Admin

TypeScript

Hybrid

@lakehouse/memory-mcp

Persistent memory for AI assistants via Model Context Protocol (MCP).

Give your AI assistant a memory that persists across conversations. Works with Claude Code, Claude Desktop, Cursor, Windsurf, and any MCP-compatible client.

Features

Remember - Store facts, preferences, tasks, and context
Recall - Semantic search to find relevant memories
Forget - Remove outdated information
Two modes:
- Lakehouse42 (recommended) - Full semantic search, deduplication, knowledge graph
- Local - Simple file-based storage with keyword search

Related MCP server: engram

Quick Start

Claude Code

Add to ~/.claude/claude_code_config.json:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["@lakehouse/memory-mcp"]
    }
  }
}

That's it! Claude Code now has persistent memory using local storage.

With Lakehouse42 Backend (Recommended)

For full semantic search capabilities, connect to Lakehouse42:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["@lakehouse/memory-mcp"],
      "env": {
        "LH42_URL": "https://api.lakehouse42.com",
        "LH42_API_KEY": "lh42_your_api_key"
      }
    }
  }
}

Get your API key at lakehouse42.com.

Claude Desktop

Add to Claude Desktop's config (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["@lakehouse/memory-mcp"],
      "env": {
        "LH42_URL": "https://api.lakehouse42.com",
        "LH42_API_KEY": "lh42_your_api_key"
      }
    }
  }
}

Cursor / Windsurf

Follow the same pattern - add the MCP server to your client's configuration.

Tools

`remember`

Store a memory for later recall.

Remember that the user prefers dark mode

Parameters:

content (required) - The information to remember
type - fact, preference, task, event, context, reflection
importance - 0.0 to 1.0 (default: 0.5)

`recall`

Search memories by semantic similarity.

What are the user's preferences?

Parameters:

query (required) - What to search for
limit - Max results (default: 5)
types - Filter by memory types

`forget`

Delete a memory by ID.

Parameters:

memoryId (required) - ID of memory to delete
reason - Reason for deletion

`list_memories`

List recent memories.

Parameters:

limit - Max results (default: 10)

`memory_status`

Check memory system status and backend info.

Local vs Lakehouse42

Feature	Local	Lakehouse42
Persistence	✅ JSON file	✅ Cloud
Search	Keyword matching	Semantic (AI-powered)
Deduplication	❌	✅ 3-tier
Knowledge graph	❌	✅ Entity relationships
History tracking	❌	✅ Full audit trail
Multi-device sync	❌	✅

Environment Variables

Variable	Description
`LH42_URL`	Lakehouse42 API URL (enables LH42 backend)
`LH42_API_KEY`	API key for authentication
`DEBUG`	Enable debug logging (`true`/`false`)

Programmatic Usage

import { createMemoryServer, LH42Backend } from "@lakehouse/memory-mcp";

// Create server with custom config
const server = await createMemoryServer({
  lh42Url: "https://api.lakehouse42.com",
  apiKey: "lh42_xxx",
  debug: true,
});

// Or use backends directly
const backend = new LH42Backend({
  url: "https://api.lakehouse42.com",
  apiKey: "lh42_xxx",
});

await backend.initialize();
await backend.remember({ content: "User likes TypeScript" });
const results = await backend.recall({ query: "programming preferences" });

Privacy

Local mode: All data stored in ~/.lakehouse42/memory-mcp/memories.json
Lakehouse42 mode: Data stored securely in your Lakehouse42 account
No data is sent to third parties
You control your memories

License

MIT

Available Tools

5 tools

forgetA

Delete a specific memory by ID. Use when information is outdated or incorrect.

ParametersJSON Schema

Name	Required	Description	Default
`reason`	No	Reason for deletion (for audit trail)
`memoryId`	Yes	ID of the memory to delete

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. The word 'Delete' clearly indicates destructive behavior, but the description does not disclose irreversibility, audit trail implications, or any side effects. It is not misleading, but lacks rich context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence followed by a usage note. It is front-loaded with the action and object, with zero wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple delete tool with fully documented parameters and no output schema, the description covers purpose, scope, and a usage condition. It lacks only explicit mention of return value or side effects, but these are not critical for this tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully covers both parameters (memoryId and reason) with descriptions, so the baseline is 3. The tool description does not add additional parameter information beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool deletes a memory by ID, using the specific verb 'Delete' and identifying the resource. This distinguishes it from sibling tools like remember, recall, and list_memories.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use when information is outdated or incorrect,' providing a clear context for when to invoke this tool. It does not explicitly list alternatives or exclusion criteria, but the guidance is sufficient for a simple deletion action.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_memoriesB

List recent memories. Useful for reviewing what has been remembered.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of memories to return (default: 10)

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must carry the full burden of disclosure, and it is insufficient. It only states 'list recent memories' without explaining ordering, return format, potential pagination, or any side effects (expected to be none). The agent gains minimal insight into the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences and gets to the point immediately. It avoids unnecessary verbosity, though it is slightly under-specified in terms of detail. It is concise rather than overly terse, deserving a score above baseline.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description should explain what the response looks like or any limitations. It does not mention return structure, ordering, or the effect of the limit parameter. Given the tool's simplicity, the description remains incomplete for an agent to fully anticipate behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully documents the single 'limit' parameter with a clear description and default, covering 100% of schema semantics. The description adds no extra parameter context, so it meets the baseline without compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists recent memories with a specific verb and resource. It implicitly distinguishes from siblings like remember (create) and forget (delete), but does not explicitly differentiate from recall, which could also retrieve memories.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Useful for reviewing what has been remembered' gives a general use case, but there is no explicit guidance on when to use this over alternatives like recall, nor exclusions. The intended context is implied rather than made precise.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

memory_statusA

Get status of the memory system including backend type and features available.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It clearly indicates a read-only operation ('Get') and previews the return content ('backend type and features'). No side effects or prerequisites are mentioned, but for a status tool this is reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that effectively communicates the purpose and key inclusions. Every word contributes, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, no annotations, and no output schema, the description provides the essential purpose (status check) and indicates the output content (backend type, features). It does not detail the exact return structure or error behavior, but for a simple status tool this is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the baseline is 4. The description correctly adds no parameter-specific information since there are none to describe, making it sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Get') and resource ('status of the memory system'), and clarifies the content by listing 'backend type and features available.' This clearly distinguishes it from sibling tools (remember, recall, forget, list_memories) which focus on individual memory operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (to check memory system status) but does not explicitly state alternatives or exclusion criteria. It lacks contrast with sibling tools, though the purpose is clear enough for a status tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recallA

Search memories by semantic similarity. Returns the most relevant memories matching the query.

ParametersJSON Schema

Name	Required	Description
`limit`	No	Maximum number of memories to return (default: 5)
`query`	Yes	What to search for in memories
`types`	No	Filter by memory types

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly states the search is based on semantic similarity and that results are ranked by relevance ('most relevant'). This adds important context beyond the schema, though it does not mention ordering details or edge cases like empty results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that immediately states the action, method, and result. No wasted words; it earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, the description covers the core behavior and return value ('returns the most relevant memories'). It lacks mention of result ordering details or relationship to sibling tools, but these are either implied or covered by the schema. The description is sufficiently complete for a search tool with well-documented parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with descriptions for all three parameters (query, limit, types). The description adds no additional parameter-specific semantics beyond the schema, but as the baseline for high coverage is 3, this is adequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Search') and resource ('memories') with a clear method ('semantic similarity'), which distinctly separates it from siblings like list_memories, remember, and forget. The purpose is immediately obvious and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'semantic similarity' implies this is the tool for similarity-based search over memories, which helps when you need relevant matches rather than exact listing. However, there is no explicit guidance on when to use this versus list_memories, nor any mention of alternatives or exclusions. The usage context is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

rememberA

Store a memory for later recall. Use this to remember important facts, user preferences, decisions, or any information that should persist across conversations.

ParametersJSON Schema

Name	Required	Description
`type`	No	Type of memory: fact (default), preference, task, event, context, or reflection
`content`	Yes	The information to remember
`importance`	No	Importance from 0.0 to 1.0 (default: 0.5). Higher importance memories are prioritized in recall.

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that memories 'persist across conversations' and are 'for later recall,' which are key behavioral traits. It does not mention failure modes, deduplication, or capacity limits, but for a simple store operation this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences, front-loaded with the key action 'Store a memory.' Every phrase adds value, and it avoids restating schema properties or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 3-parameter tool with no output schema, the description covers the primary purpose, persistence behavior, and typical use cases. It is complete enough for an agent to select and invoke the tool correctly, though it omits potential edge cases like error handling or memory size limits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the description does not need to document parameters. The examples in the description map to the 'content' parameter, but the tool adds no extra semantic detail beyond the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Store a memory for later recall.' It lists concrete examples (facts, preferences, decisions) that clarify scope and distinguish it from sibling tools like recall and forget, which are retrieval/deletion operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this to remember...' and enumerates clear use cases such as important facts, preferences, and decisions. It does not explicitly name alternatives or exclusion scenarios, but the use cases strongly imply when this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

5 tool updatesv0.1.1Aug 9, 2026
- First observedforget
- First observedlist_memories
- First observedmemory_status
- First observedrecall
- First observedremember

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored 2026-08-09 17:35 across 5 tools

Disambiguation5/5

Each tool targets a distinct action: remember stores, recall searches, forget deletes, list_memories lists, and memory_status reports system status. There is no ambiguity or overlap between them.

Naming Consistency4/5

Names are mostly verb-based and readable, but there is a slight inconsistency: remember, recall, and forget are bare verbs, while list_memories is verb_noun and memory_status is noun_noun. A more uniform pattern like get_memory_status would improve consistency.

Tool Count5/5

With 5 tools, the server is well-scoped for a memory system. Each tool covers a necessary function without redundancy, making the set neither too thin nor overly heavy.

Completeness4/5

The tools cover the core CRUD operations (create, read, delete) plus status. The only minor gap is the lack of an explicit update tool, but users can forget and re-remember to modify entries, so the shortfall is easily worked around.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

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@lakehouse/memory-mcp

Features

Quick Start

Claude Code

With Lakehouse42 Backend (Recommended)

Claude Desktop

Cursor / Windsurf

Tools

remember

recall

forget

list_memories

memory_status

Local vs Lakehouse42

Environment Variables

Programmatic Usage

Privacy

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

LedgerMem MCP Serverofficial

`remember`

`recall`

`forget`

`list_memories`

`memory_status`