Mem0 MCP Server
OfficialThe Mem0 MCP Server provides long-term memory management for AI applications, enabling MCP-compatible clients (Claude Desktop, Cursor, VS Code, custom agents) to store, retrieve, search, update, and delete memories for users, agents, apps, and runs.
Add memories (
add_memory): Save text, facts, conversation history, or structured messages, scoped to a user/agent/app/run, with optional metadata and graph memory support.Search memories (
search_memories): Semantic natural language search across stored memories with filters (user, agent, date range, etc.) and configurable result limits.List memories (
get_memories): Browse memories using structured filters and pagination (page number + page size).Fetch a single memory (
get_memory): Retrieve one specific memory by itsmemory_id.Update a memory (
update_memory): Overwrite the text of an existing memory by itsmemory_id.Delete a memory (
delete_memory): Remove a single memory by itsmemory_id.Bulk delete memories (
delete_all_memories): Delete all memories within a scope (user/agent/app/run) while keeping the entity record.Delete entities (
delete_entities): Cascade-delete a user, agent, app, or run record along with all associated memories.List entities (
list_entities): Enumerate all users, agents, apps, and runs currently stored in the system.
The server can be deployed as a Python package, Docker container, or via Smithery remote hosting.
Manages environment variables to securely store API keys, including mem0 API key configuration.
Provides demo capabilities through GitHub assets, demonstrating the coding preferences functionality in action.
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., "@Mem0 MCP Serversearch for my Python coding style preferences"
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.
This project has been archived
mem0-mcp-server is no longer actively maintained and this repository is now a public archive.
Thank you to the 640+ stargazers, 140+ forkers, and every contributor who helped shape this project. Your support and feedback meant the world to us.
Looking for Mem0 MCP? We now offer an official cloud-hosted MCP server. Check out the docs to get started.
Quick install across all major clients:
npx mcp-add \
--name mem0-mcp \
--type http \
--url "https://mcp.mem0.ai/mcp" \
--clients "claude,claude code,cursor,windsurf,vscode,opencode"Mem0 MCP Server
mem0-mcp-server wraps the official Mem0 Memory API as a Model Context Protocol (MCP) server so any MCP-compatible client (Claude Desktop, Cursor, custom agents) can add, search, update, and delete long-term memories.
Related MCP server: Doclea MCP
Tools
The server exposes the following tools to your LLM:
Tool | Description |
| Save text or conversation history (or explicit message objects) for a user/agent. |
| Semantic search across existing memories (filters + limit supported). |
| List memories with structured filters and pagination. |
| Retrieve one memory by its |
| Overwrite a memory's text once the user confirms the |
| Delete a single memory by |
| Bulk delete all memories in the confirmed scope (user/agent/app/run). |
| Delete a user/agent/app/run entity (and its memories). |
| Enumerate users/agents/apps/runs stored in Mem0. |
All responses are JSON strings returned directly from the Mem0 API.
Usage Options
There are three ways to use the Mem0 MCP Server:
Python Package - Install and run locally using
uvxwith any MCP clientDocker - Containerized deployment that creates an
/mcpHTTP endpointSmithery - Remote hosted service for managed deployments
Quick Start
Installation
uv pip install mem0-mcp-serverOr with pip:
pip install mem0-mcp-serverClient Configuration
Add this configuration to your MCP client:
{
"mcpServers": {
"mem0": {
"command": "uvx",
"args": ["mem0-mcp-server"],
"env": {
"MEM0_API_KEY": "m0-...",
"MEM0_DEFAULT_USER_ID": "your-handle"
}
}
}
}Test with the Python Agent
To test the server immediately, use the included Pydantic AI agent:
# Install the package
pip install mem0-mcp-server
# Or with uv
uv pip install mem0-mcp-server
# Set your API keys
export MEM0_API_KEY="m0-..."
export OPENAI_API_KEY="sk-openai-..."
# Clone and test with the agent
git clone https://github.com/mem0ai/mem0-mcp.git
cd mem0-mcp-server
python example/pydantic_ai_repl.pyUsing different server configurations:
# Use with Docker container
export MEM0_MCP_CONFIG_PATH=example/docker-config.json
export MEM0_MCP_CONFIG_SERVER=mem0-docker
python example/pydantic_ai_repl.py
# Use with Smithery remote server
export MEM0_MCP_CONFIG_PATH=example/config-smithery.json
export MEM0_MCP_CONFIG_SERVER=mem0-memory-mcp
python example/pydantic_ai_repl.pyWhat You Can Do
The Mem0 MCP server enables powerful memory capabilities for your AI applications:
Remember that I'm allergic to peanuts and shellfish - Add new health information to memory
Store these trial parameters: 200 participants, double-blind, placebo-controlled study - Save research data
What do you know about my dietary preferences? - Search and retrieve all food-related memories
Update my project status: the mobile app is now 80% complete - Modify existing memory with new info
Delete all memories from 2023, I need a fresh start - Bulk remove outdated memories
Show me everything I've saved about the Phoenix project - List all memories for a specific topic
Configuration
Environment Variables
MEM0_API_KEY(required) – Mem0 platform API key.MEM0_DEFAULT_USER_ID(optional) – defaultuser_idinjected into filters and write requests (defaults tomem0-mcp).MEM0_ENABLE_GRAPH_DEFAULT(optional) – Enable graph memories by default (defaults tofalse).MEM0_MCP_AGENT_MODEL(optional) – default LLM for the bundled agent example (defaults toopenai:gpt-4o-mini).
Advanced Setup
Docker Deployment
To run with Docker:
Build the image:
docker build -t mem0-mcp-server .Run the container:
docker run --rm -d \ --name mem0-mcp \ -e MEM0_API_KEY=m0-... \ -p 8080:8081 \ mem0-mcp-serverMonitor the container:
# View logs docker logs -f mem0-mcp # Check status docker ps
Running with Smithery Remote Server
To connect to a Smithery-hosted server:
Install the MCP server (Smithery dependencies are now bundled):
pip install mem0-mcp-serverConfigure MCP client with Smithery:
{ "mcpServers": { "mem0-memory-mcp": { "command": "npx", "args": [ "-y", "@smithery/cli@latest", "run", "@mem0ai/mem0-memory-mcp", "--key", "your-smithery-key", "--profile", "your-profile-name" ], "env": { "MEM0_API_KEY": "m0-..." } } } }
Development Setup
Clone and run from source:
git clone https://github.com/mem0ai/mem0-mcp.git
cd mem0-mcp-server
pip install -e ".[dev]"
# Run locally
mem0-mcp-server
# Or with uv
uv sync
uv run mem0-mcp-serverLicense
Available Tools
9 toolsadd_memoryB
Store a new preference, fact, or conversation snippet. Requires at least one: user_id, agent_id, or run_id.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Plain sentence summarizing what to store. Required even if `messages` is provided. | |
| messages | No | Structured conversation history with `role`/`content`. Use when you have multiple turns. | |
| user_id | No | Override the default user scope for this write. | |
| agent_id | No | Optional agent identifier. | |
| app_id | No | Optional app identifier. | |
| run_id | No | Optional run identifier. | |
| metadata | No | Attach arbitrary metadata JSON to the memory. | |
| enable_graph | No | Set true only if the caller explicitly wants Mem0 graph memory. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the requirement for identifiers but lacks details on permissions, rate limits, whether the operation is idempotent, or what happens on success/failure. For a write operation with no annotation coverage, this leaves significant gaps in understanding 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences that directly convey the core purpose and a key requirement. Every word serves a purpose, and it's front-loaded with the main action, making it efficient and easy to parse.
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 complexity of 8 parameters, no annotations, but with a rich input schema (100% coverage) and an output schema present, the description is minimally adequate. It covers the basic purpose and a critical requirement but doesn't address behavioral aspects like error handling or performance, leaving room for improvement despite the structured support.
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 schema already documents all parameters thoroughly. The description adds minimal value by hinting at the identifier requirement but doesn't provide additional semantic context beyond what's in the schema, such as examples or edge cases. This meets the baseline for high schema coverage.
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 action ('Store') and resource type ('preference, fact, or conversation snippet'), making the purpose evident. It doesn't explicitly differentiate from sibling tools like 'update_memory' or 'get_memories', but the verb 'Store a new' implies creation rather than modification or 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?
The description provides some context by specifying that at least one identifier (user_id, agent_id, or run_id) is required, which implies usage when scoping a memory. However, it doesn't explicitly state when to use this tool versus alternatives like 'update_memory' for modifications or 'get_memories' for retrieval, leaving the guidelines somewhat implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_all_memoriesC
Delete every memory in the given user/agent/app/run but keep the entity.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | No | User scope to delete; defaults to server user. | |
| agent_id | No | Optional agent scope to delete. | |
| app_id | No | Optional app scope to delete. | |
| run_id | No | Optional run scope to delete. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 the destructive action ('Delete every memory') and clarifies that entities are preserved ('keep the entity'), which is useful. However, it lacks critical details: no mention of permissions required, irreversibility, confirmation prompts, rate limits, or what the output schema returns. For a high-impact deletion 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, efficient sentence that front-loads the core action ('Delete every memory') and adds clarifying context ('but keep the entity'). There is zero waste or redundancy, making it highly concise and well-structured for quick comprehension.
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 high complexity (destructive bulk deletion) and lack of annotations, the description is incomplete. It covers the basic action and entity preservation but omits safety warnings, permissions, irreversibility, and output details. The presence of an output schema helps, but the description should do more to contextualize this risky operation.
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%, with each parameter well-documented in the schema (e.g., 'User scope to delete; defaults to server user'). The description adds no parameter-specific details beyond implying scoping ('in the given user/agent/app/run'), which the schema already covers. Baseline 3 is appropriate as the schema does the heavy lifting.
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 action ('Delete every memory') and the target resource ('in the given user/agent/app/run'), which is specific and actionable. It distinguishes from siblings like 'delete_memory' (singular) and 'delete_entities' (different resource). However, it doesn't explicitly contrast with all siblings (e.g., 'search_memories' for finding vs. deleting).
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 no guidance on when to use this tool versus alternatives like 'delete_memory' (for single deletions) or 'delete_entities' (for different resources). It mentions keeping the entity, but doesn't clarify when bulk deletion is appropriate or warn against misuse. No explicit when/when-not instructions are included.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_entitiesA
Remove a user/agent/app/run record entirely (and cascade-delete its memories).
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | No | Delete this user and its memories. | |
| agent_id | No | Delete this agent and its memories. | |
| app_id | No | Delete this app and its memories. | |
| run_id | No | Delete this run and its memories. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: the tool performs a destructive operation ('Remove entirely'), specifies what gets deleted (entities and their memories via cascade), and identifies the types of entities affected. However, it lacks details on permissions, rate limits, or error conditions.
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, efficient sentence that front-loads the core action and scope. Every word earns its place by specifying the verb, resources, and cascade effect without redundancy or unnecessary elaboration.
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 destructive nature, no annotations, and an output schema (which handles return values), the description is reasonably complete. It covers the purpose, scope, and cascade behavior, though it could benefit from more explicit warnings about irreversibility or usage guidelines relative to siblings.
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%, with each parameter documented in the schema. The description adds value by clarifying that parameters are mutually exclusive (implied by 'a user/agent/app/run record' singular) and that deletion cascades to memories, but does not provide additional syntax or format details beyond the schema.
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 specific action ('Remove entirely') and identifies the target resources ('user/agent/app/run record'), distinguishing it from sibling tools like delete_memory or delete_all_memories. It also specifies the cascade effect on memories, which adds important context about the scope of deletion.
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 implies usage for permanent removal of entities with memory cascading, but does not explicitly state when to use this versus alternatives like delete_memory (for individual memories) or delete_all_memories (for all memories without entity deletion). No exclusions 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.
delete_memoryB
Delete one memory after the user confirms its memory_id.
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | Exact memory_id to delete. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions user confirmation requirement, which is valuable behavioral context. However, it lacks details on permissions needed, whether deletion is reversible, rate limits, or what happens upon success/failure. For a destructive operation with zero annotation coverage, 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, clear sentence that efficiently conveys the core action and key constraint (user confirmation). It's front-loaded with the main purpose and wastes no words.
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 has an output schema (not provided in details), the description doesn't need to explain return values. However, as a destructive operation with no annotations, it should provide more behavioral context about permissions, reversibility, or error handling. The description is adequate but has clear gaps for a deletion 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?
Schema description coverage is 100%, with the parameter memory_id documented as 'Exact memory_id to delete.' The description adds minimal value by restating this parameter in context, but doesn't provide additional syntax, format examples, or constraints beyond what the schema already covers.
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 action ('Delete') and resource ('one memory'), specifying it operates on a single memory identified by memory_id. It distinguishes from siblings like delete_all_memories by focusing on single deletion, but doesn't explicitly contrast with other deletion tools like delete_entities.
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 implies usage when a user wants to delete a specific memory after confirmation, but doesn't provide explicit guidance on when to use this versus alternatives like delete_all_memories or delete_entities. No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_memoriesA
Page through memories using filters instead of search.
Use filters to list specific memories. Common filter patterns:
- Single user: {"AND": [{"user_id": "john"}]}
- Agent memories: {"AND": [{"agent_id": "agent_name"}]}
- Recent memories: {"AND": [{"user_id": "john"}, {"created_at": {"gte": "2024-01-01"}}]}
- Multiple users: {"AND": [{"user_id": {"in": ["john", "jane"]}}]}
Pagination: Use page (1-indexed) and page_size for browsing results.
user_id is automatically added to filters if not provided.
| Name | Required | Description | Default |
|---|---|---|---|
| filters | No | Structured filters; user_id injected automatically. | |
| page | No | 1-indexed page number when paginating. | |
| page_size | No | Number of memories per page (default 10). | |
| enable_graph | No | Set true only if the caller explicitly wants graph-derived memories. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 effectively describes key behaviors: pagination mechanics (page and page_size), automatic user_id injection into filters, and the default page_size of 10. However, it doesn't mention rate limits, authentication requirements, or error conditions, leaving some gaps.
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 well-structured and front-loaded with the core purpose, followed by usage guidelines and parameter semantics. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.
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 moderate complexity (4 parameters, no annotations, but with an output schema), the description is largely complete. It covers purpose, usage, key behaviors, and parameter details. The presence of an output schema means return values don't need explanation, but minor gaps remain in behavioral aspects like error handling or advanced usage notes.
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 baseline is 3. The description adds significant value by explaining the 'filters' parameter with concrete examples (e.g., single user, agent memories), clarifying that 'user_id is automatically added,' and noting pagination details. This enhances understanding beyond the schema's technical definitions.
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 purpose as 'Page through memories using filters instead of search,' which is a specific verb+resource+method combination. It distinguishes from sibling tools like 'search_memories' by emphasizing the filter-based approach versus search functionality.
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 explicit guidance on when to use this tool ('use filters to list specific memories') and implicitly contrasts it with 'search_memories' by stating 'instead of search.' It also offers common filter patterns as practical examples, helping the agent understand appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_memoryB
Fetch a single memory once you know its memory_id.
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | Exact memory_id to fetch. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 states 'Fetch a single memory', which implies a read-only operation, but doesn't disclose behavioral traits such as error handling (e.g., what happens if memory_id is invalid), authentication needs, rate limits, or response format. This leaves significant gaps for a tool with no annotation coverage.
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, efficient sentence with zero waste. It front-loads the key information ('Fetch a single memory') and adds a concise usage note ('once you know its memory_id'), making it appropriately sized and well-structured.
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 (1 parameter, 100% schema coverage, and an output schema exists), the description is mostly complete. It clarifies the tool's purpose and basic usage. However, without annotations, it could benefit from more behavioral context, but the output schema reduces the need to explain return values, keeping it adequate.
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 schema description coverage is 100%, with the parameter 'memory_id' well-documented as 'Exact memory_id to fetch'. The description adds minimal value beyond this, only reinforcing that the memory_id must be known. Since the schema does the heavy lifting, 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 clearly states the action ('Fetch') and resource ('a single memory'), distinguishing it from sibling tools like 'get_memories' (plural) and 'search_memories'. However, it doesn't explicitly differentiate from 'list_entities' or 'delete_memory', which could also involve memory operations, leaving some ambiguity.
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 implies usage by stating 'once you know its memory_id', suggesting it's for retrieving specific memories rather than listing or searching. However, it lacks explicit guidance on when to use this versus alternatives like 'get_memories' or 'search_memories', and doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_entitiesB
List which users/agents/apps/runs currently hold memories.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 the tool lists entities holding memories but doesn't cover aspects like whether it's read-only, pagination, rate limits, or error handling. This leaves significant gaps for a tool that interacts with memory data.
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, clear sentence that directly states the tool's function without any fluff. It's front-loaded and efficiently communicates the core purpose, making it highly concise and well-structured.
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 has 0 parameters and an output schema exists, the description's job is simplified. It adequately states what the tool does, but for a memory-related tool with no annotations, it could benefit from more context on behavior (e.g., read-only nature, data format). It meets the minimum for this complexity level but has room for improvement.
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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't add parameter details, earning a high score for not introducing unnecessary information.
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 action ('List') and the resource ('users/agents/apps/runs currently hold memories'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'get_memories' or 'search_memories', which might also retrieve memory-related data, so it misses full sibling distinction.
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 no guidance on when to use this tool versus alternatives such as 'get_memories' or 'search_memories'. It lacks context on use cases, exclusions, or prerequisites, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_memoriesA
Run a semantic search over existing memories.
Use filters to narrow results. Common filter patterns:
- Single user: {"AND": [{"user_id": "john"}]}
- Agent memories: {"AND": [{"agent_id": "agent_name"}]}
- Recent memories: {"AND": [{"user_id": "john"}, {"created_at": {"gte": "2024-01-01"}}]}
- Multiple users: {"AND": [{"user_id": {"in": ["john", "jane"]}}]}
- Cross-entity: {"OR": [{"user_id": "john"}, {"agent_id": "agent_name"}]}
user_id is automatically added to filters if not provided.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language description of what to find. | |
| filters | No | Additional filter clauses (user_id injected automatically). | |
| limit | No | Maximum number of results to return. | |
| enable_graph | No | Set true only when the user explicitly wants graph-derived memories. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds valuable behavioral context. It discloses that 'user_id is automatically added to filters if not provided' - an important implementation detail not evident from the schema. It also provides practical filter patterns showing how the tool behaves with different query structures. However, it doesn't mention rate limits, authentication requirements, or pagination 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 appropriately front-loaded with the core purpose, but the extensive filter examples (5 patterns) make it somewhat lengthy. While the examples are helpful, they could potentially be streamlined. Every sentence earns its place by providing practical guidance, but the structure could be more concise.
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 (semantic search with filtering), no annotations, but with complete schema coverage and an output schema, the description provides good contextual coverage. It explains the core search behavior, provides practical filter examples, and discloses the automatic user_id injection. The presence of an output schema means the description doesn't need to explain return values. However, for a search tool with no annotations, it could benefit from mentioning performance characteristics or result ordering.
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?
With 100% schema description coverage, the baseline is 3. The description adds meaningful value by explaining filter usage patterns with concrete examples, showing how the 'filters' parameter works in practice. It also clarifies the automatic user_id injection behavior, which enhances understanding beyond the schema's technical description. However, it doesn't provide similar context for 'enable_graph' or 'limit' 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's purpose: 'Run a semantic search over existing memories.' This specifies the verb ('search') and resource ('memories'), and the semantic aspect distinguishes it from simple filtering. However, it doesn't explicitly differentiate from sibling tools like 'get_memories' or 'list_entities'.
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 implied usage context through filter examples and the note about automatic user_id injection, suggesting this is for retrieving memories with semantic matching. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_memories' (which appears to be a simpler retrieval) or 'list_entities'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_memoryB
Overwrite an existing memory’s text.
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | Exact memory_id to overwrite. | |
| text | Yes | Replacement text for the memory. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'overwrite' which implies a destructive mutation, but doesn't disclose behavioral traits like permission requirements, whether the operation is reversible, rate limits, or what happens to the old text. For a mutation tool with zero annotation coverage, this is a significant gap.
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, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.
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 that there's an output schema (which covers return values), no annotations, and high schema coverage, the description is minimally adequate. However, as a mutation tool with no behavioral disclosure, it lacks completeness for safe and effective use by an AI agent.
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 schema already documents both parameters ('memory_id' and 'text') thoroughly. The description adds no additional meaning beyond what the schema provides, such as format details or constraints, but doesn't need to compensate for gaps.
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 action ('Overwrite') and resource ('an existing memory's text'), making the purpose immediately understandable. It doesn't explicitly distinguish from siblings like 'add_memory' or 'delete_memory', but the verb 'overwrite' implies modification rather than creation or removal.
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 guidance is provided on when to use this tool versus alternatives like 'add_memory' or 'delete_memory'. The description only states what it does without context about prerequisites, timing, or comparisons to sibling tools.
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.
9 tool updates
- First observed
add_memory - First observed
delete_all_memories - First observed
delete_entities - First observed
delete_memory - First observed
get_memories - First observed
get_memory - First observed
list_entities - First observed
search_memories - First observed
update_memory
TDQS
Scored across 9 tools
Most tools have distinct purposes, but get_memories and search_memories could cause confusion as both involve retrieving memories with filters, though search_memories adds semantic search. The other tools (add, delete, update, list) are clearly differentiated by their actions on memories or entities.
All tools follow a consistent verb_noun pattern (e.g., add_memory, delete_memory, get_memories), with clear and predictable naming. There are no deviations in style or convention across the set.
With 9 tools, this server is well-scoped for memory management, covering core operations like CRUD for memories and entities, plus search and listing. Each tool serves a clear purpose without redundancy or bloat.
The toolset provides complete coverage for memory management: create (add_memory), read (get_memory, get_memories, search_memories), update (update_memory), delete (delete_memory, delete_all_memories, delete_entities), and list (list_entities). No obvious gaps exist for the domain.
Maintenance
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Persistent memory and cross-session learning for AI coding assistants (hosted remote MCP).
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