Memory Box MCP Server
The Memory Box MCP Server provides tools to interact with Memory Box for semantic memory management:
Save Memories: Store formatted memories with metadata, source information, and customizable buckets
Search Memories: Perform semantic searches to find relevant memories
Retrieve Memories: Fetch all memories or from specific buckets
Format Memories: Structure text according to predefined or customized system prompts
Get Usage Stats: View metrics, plan information, and resource limits
Find Similar Memories: Discover semantically similar memories based on a memory ID
Check Processing Status: Monitor the processing status of specific memories
Configuration Options: Customize system prompts and other settings via environment variables
Click on "Install 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., "@Memory Box MCP Serversave that vector databases store high-dimensional vectors for semantic search"
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 MCP server provides tools for interacting with a Memory Box instance, allowing you to save and search memories using semantic search directly from Cline and Claude Desktop.
Related Projects
This MCP server is designed to work with Memory Box, a semantic memory storage and retrieval system powered by vector embeddings.
Memory Box provides the backend API that this MCP server communicates with, allowing you to:
Store memories with vector embeddings for semantic search
Organize memories into customizable buckets
Search for memories based on meaning, not just keywords
Retrieve memories with detailed context
Find semantically related memories
Track memory processing status
For more information about Memory Box, including how to set up your own instance, please visit the Memory Box website.
Related MCP server: mcp-memory
Features
Save Memories: Save formatted memories to your Memory Box with source information and metadata
Search Memories: Search your memories using semantic search with pagination and date sorting
Retrieve Memories: Get all memories or memories from specific buckets
Bucket Management: Create and delete buckets for organizing memories
Memory Management: Update or delete existing memories
Find Related Memories: Discover semantically similar memories
Check Memory Status: Monitor the processing status of your memories
Format Memories: Format memories according to a structured system prompt
Usage Statistics: View your current plan, usage metrics, and resource limits
Installation
The server has been installed and configured for use with Cline. Note that you need a running Memory Box instance (either self-hosted or using the hosted version at memorybox.amotivv.ai) to use this MCP server.
Installing as Claude Desktop Extension (Recommended)
The easiest way to use Memory Box with Claude Desktop is through the Desktop Extension:
Download the latest
memory-box.mcpbfile from the releases pageOpen Claude Desktop
Go to Settings > Extensions
Click "Install from file"
Select the downloaded
memory-box.mcpbfileConfigure your Memory Box API token in the extension settings
The extension will automatically configure all necessary environment variables and tools.
Installing via Smithery
To install Memory Box MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @amotivv/memory-box-mcp --client claudeTo complete the setup:
Edit the Cline MCP settings file at:
~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.jsonAdd your Memory Box token to the
MEMORY_BOX_TOKENenvironment variable:"memory-box-mcp": { "command": "node", "args": [ "<path-to-repository>/build/index.js" ], "env": { "MEMORY_BOX_API_URL": "https://memorybox.amotivv.ai", "MEMORY_BOX_TOKEN": "your-token-here", "DEFAULT_BUCKET": "General" }, "disabled": false, "autoApprove": [] }Optionally, you can customize the default bucket by changing the
DEFAULT_BUCKETvalue.
Usage
Once configured, you can use the following tools in Cline:
Save Memory
Save a memory to Memory Box with proper formatting:
Use the save_memory tool to save this information about vector databases: "Vector databases like pgvector store and query high-dimensional vectors for semantic search applications."Parameters:
text(required): The memory content to savebucket_id(optional): The bucket to save the memory to (default: "General")format(optional): Whether to format the memory according to the system prompt (default: true)type(optional): The type of memory (TECHNICAL, DECISION, SOLUTION, CONCEPT, REFERENCE, APPLICATION, FACT) for formatting (default: "TECHNICAL")source_type(optional): Type of memory source (default: "llm_plugin")reference_data(optional): Additional metadata about the memory source and context
Search Memories
Search for memories using semantic search:
Use the search_memories tool to find information about "vector databases"Parameters:
query(required): The search querydebug(optional): Include debug information in results (default: false)
Get All Memories
Retrieve all memories:
Use the get_all_memories tool to show me all my saved memoriesGet Bucket Memories
Get memories from a specific bucket:
Use the get_bucket_memories tool to show me memories in the "Learning" bucketParameters:
bucket_id(required): The bucket to retrieve memories from
Format Memory
Format a text according to the memory system prompt without saving:
Use the format_memory tool to format this text: "Vector databases like pgvector store and query high-dimensional vectors for semantic search applications."Parameters:
text(required): The text to formattype(optional): The type of memory (TECHNICAL, DECISION, SOLUTION, CONCEPT, REFERENCE, APPLICATION, FACT) (default: "TECHNICAL")
Get Related Memories
Find semantically similar memories to a specific memory:
Use the get_related_memories tool with memory ID 123Parameters:
memory_id(required): The ID of the memory to find related memories formin_similarity(optional): Minimum similarity threshold (0.0-1.0) for related memories (default: 0.7)
Check Memory Status
Check the processing status of a memory:
Use the check_memory_status tool with memory ID 123Parameters:
memory_id(required): The ID of the memory to check status for
Get Usage Stats
Retrieve user usage statistics and plan information:
Use the get_usage_stats tool to show me my current plan and usage metricsThis tool returns:
Current plan information (e.g., free, basic, professional, legacy)
User status and limit enforcement information
Current month usage metrics (store operations, search operations, API calls)
Data processing volume with human-readable formatting
Resource limits based on your plan (if applicable)
Operation breakdown by type
No parameters are required for this operation.
Get Buckets
List all available buckets:
Use the get_buckets tool to show me all available bucketsThis tool returns a list of all buckets with their names, IDs, memory counts, and creation dates.
Create Bucket
Create a new bucket for organizing memories:
Use the create_bucket tool to create a bucket named "Project Ideas"Parameters:
bucket_name(required): Name of the bucket to create
Delete Bucket
Delete an existing bucket:
Use the delete_bucket tool to delete the bucket named "Old Notes"Parameters:
bucket_name(required): Name of the bucket to deleteforce(optional): Force deletion even if bucket contains memories (default: false)
Update Memory
Update an existing memory's content, bucket, or metadata:
Use the update_memory tool to update memory ID 123 with new text: "Updated information about vector databases"Parameters:
memory_id(required): The ID of the memory to updatetext(optional): New text content for the memorybucket_id(optional): New bucket for the memoryreference_data(optional): Updated reference data including relationships
Delete Memory
Delete a specific memory:
Use the delete_memory tool to delete memory ID 123Parameters:
memory_id(required): The ID of the memory to delete
Customization
System Prompt Customization
The Memory Box MCP server uses a system prompt to format memories according to specific guidelines. You can customize this prompt to change how memories are formatted.
Default System Prompt
The default system prompt includes formatting guidelines for different types of memories:
You are a helpful AI assistant. When storing memories with Memory Box, follow these enhanced formatting guidelines:
1. STRUCTURE: Format memories based on the type of information:
- TECHNICAL: "TECHNICAL - [Brief topic]: [Concise explanation with specific details]"
- DECISION: "DECISION - [Brief topic]: [Decision made] because [rationale]. Alternatives considered: [options]."
- SOLUTION: "SOLUTION - [Problem summary]: [Implementation details that solved the issue]"
- CONCEPT: "CONCEPT - [Topic]: [Clear explanation of the concept with examples]"
- REFERENCE: "REFERENCE - [Topic]: [URL, tool name, or resource] for [specific purpose]"
- APPLICATION: "APPLICATION - [App name]: [User-friendly description] followed by [technical implementation details]"
2. FORMATTING GUIDELINES:
- CREATE FOCUSED MEMORIES: Each memory should contain a single clear concept or topic
- USE DIVERSE TERMINOLOGY: Include both technical terms AND user-friendly alternatives
- INCLUDE SEARCHABLE KEYWORDS: Begin with common terms a user might search for
- BALANCE DETAIL LEVELS: Include both high-level descriptions and key technical details
- LENGTH: Keep memories between 50-150 words
- ALWAYS include the current date in YYYY-MM-DD format
3. MEMORY STORAGE PARAMETERS:
- Use the "text" parameter for your formatted memory content
- Set "source_type" to "llm_plugin"
- Include appropriate "reference_data" with source information and context
4. REFERENCE DATA STRUCTURE:
- source.platform: Identify your platform (e.g., "claude_desktop", "cline")
- source.type: Always set to "llm_plugin"
- source.version: Optional version information
- context.conversation_id: Include when available to link related conversation memories
- context.message_id: Optional identifier for the specific message
5. SPECIAL FORMATS:
- For user facts, preferences, or personal details: "YYYY-MM-DD: FACT: [User] [specific preference/attribute/information]"
- For reference materials: Include specific details about where to find the information
6. RELATED MEMORIES: After finding memories with search, check if there are related memories using the get_related_memories tool with the memory_id from search results. Present these additional memories to provide the user with more context.
7. RETRIEVAL CONSIDERATION: Before storing an important memory, consider: "What search terms might someone use to find this information later?" and ensure those terms are included.How to Customize the System Prompt
To customize the system prompt:
Edit the Cline MCP settings file at:
~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.jsonAdd your custom system prompt to the
SYSTEM_PROMPTenvironment variable:"memory-box-mcp": { "command": "node", "args": [ "<path-to-repository>/build/index.js" ], "env": { "MEMORY_BOX_API_URL": "https://your-memory-box-instance", "MEMORY_BOX_TOKEN": "your-token-here", "DEFAULT_BUCKET": "General", "SYSTEM_PROMPT": "Your custom system prompt here..." }, "disabled": false, "autoApprove": [] }A template file is provided at
<path-to-repository>/system-prompt-template.txtthat you can copy and modify.Restart Cline to apply the changes
System Prompt Helper
The Memory Box MCP server includes a helper script for managing the system prompt:
# View the current system prompt
cd <path-to-repository>
npm run prompt-helper -- view
# Reset to the default system prompt
cd <path-to-repository>
npm run prompt-helper -- reset
# Validate a custom system prompt
cd <path-to-repository>
npm run prompt-helper -- validateOther Configuration Options
You can also customize these environment variables:
MEMORY_BOX_API_URL: The URL of your Memory Box instanceMEMORY_BOX_TOKEN: Your authentication token for Memory BoxDEFAULT_BUCKET: The default bucket to use when saving memories
Troubleshooting
If you encounter issues:
Check that your Memory Box token is correctly configured
Verify that your Memory Box instance is running and accessible
Check the Cline logs for any error messages
Development
To make changes to the server:
Edit the source code in
<path-to-repository>/src/Rebuild the server:
cd <path-to-repository> npm run buildRestart Cline to apply the changes
Building the Desktop Extension
To build the Desktop Extension package:
Install dependencies:
npm installBuild the extension:
npm run build-extensionThe built extension will be available at
dist/memory-box.mcpb
Release Process
Update version in
package.jsonUpdate
CHANGELOG.mdwith new changesCommit changes
Create a new GitHub release
Upload the
memory-box.mcpbfile as a release asset
Available Tools
12 toolscheck_memory_statusC
Check the processing status of a memory
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory to check status for |
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 mentions 'check' and 'processing status', which suggests a read-only operation, but does not clarify authentication needs, rate limits, error conditions, or what the status values might be. This leaves significant gaps for an agent to understand how to interpret results.
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 directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently communicates the core function, making it easy for an agent 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 the lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., status values like 'processing', 'done', or error messages), nor does it address potential side effects or dependencies. For a tool that likely interacts with asynchronous processes, more context is needed for effective use.
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 100% description coverage, with 'memory_id' clearly documented as 'The ID of the memory to check status for'. The description adds no additional parameter details beyond what the schema provides, such as format constraints or examples. 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 ('check') and resource ('processing status of a memory'), making the purpose understandable. It distinguishes from siblings like 'get_all_memories' or 'get_related_memories' by focusing on status rather than content retrieval. However, it could be more specific about what 'processing status' entails (e.g., pending, completed, failed).
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 'get_all_memories' or 'search_memories'. The description implies it's for checking status after an operation (e.g., after 'save_memory'), but this is not explicitly stated, and there are no exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_bucketC
Create a new bucket for organizing memories
| Name | Required | Description | Default |
|---|---|---|---|
| bucket_name | Yes | Name of the bucket to create |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Create' implies a write operation, it doesn't specify permissions needed, whether bucket names must be unique, what happens on duplicate names, or what the tool returns. For a mutation tool with zero annotation coverage, this is inadequate.
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 gets straight to the point with no wasted words. It's appropriately sized for a simple creation tool with one parameter.
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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after creation (e.g., returns bucket ID), error conditions, or behavioral constraints. Given the complexity of a write operation, more context is needed.
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 fully documents the single parameter 'bucket_name'. The description adds no additional parameter context beyond what's in the schema, meeting 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 ('Create') and resource ('new bucket for organizing memories'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its siblings like 'get_buckets' or 'delete_bucket' beyond the obvious create vs read/delete 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. It doesn't mention prerequisites (e.g., whether buckets must be unique), when not to use it, or how it relates to sibling tools like 'get_buckets' or 'delete_bucket'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_bucketA
Delete a bucket (empty by default, use force to delete with content)
| Name | Required | Description | Default |
|---|---|---|---|
| bucket_name | Yes | Name of the bucket to delete | |
| force | No | Force deletion even if bucket contains memories (default: false) |
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 describes the default behavior (deleting empty buckets) and the effect of the 'force' parameter (deleting with content), which are useful. However, it lacks details on permissions, reversibility, error handling, or response format, leaving gaps for a mutation tool.
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 immediately clarifies the default and optional behaviors. Every word earns its place, with no redundancy or unnecessary elaboration, 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's complexity as a destructive operation with no annotations or output schema, the description is somewhat complete by covering the default and forced deletion behaviors. However, it lacks critical details like permissions, confirmation prompts, or error cases, which are important for safe usage, making it only 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%, so the schema already documents both parameters thoroughly. The description adds minimal value by implying the 'force' parameter's effect but does not provide additional syntax or format details beyond what the schema states, meeting 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 specific action ('Delete') and resource ('a bucket'), distinguishing it from sibling tools like 'create_bucket' or 'get_buckets'. It also specifies the default behavior ('empty by default') and an optional capability ('use force to delete with content'), making the purpose explicit and differentiated.
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 clear context on when to use the 'force' parameter (to delete a bucket with content) versus the default (empty bucket), which helps guide usage. However, it does not explicitly mention when to use this tool versus alternatives like 'delete_memory' or warn about prerequisites, such as checking bucket emptiness first, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_memoryC
Delete a specific memory
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory to delete |
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 'Delete' which implies a destructive mutation, but doesn't specify whether this is permanent, requires special permissions, has side effects (e.g., affecting related memories), or provides confirmation. 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 four words, front-loading the essential action and resource. There's no wasted language or unnecessary elaboration, making it efficient for quick understanding.
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 destructive mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens after deletion (e.g., success confirmation, error handling), whether the action is reversible, or how it interacts with sibling tools like 'get_all_memories'. More context is needed given the tool's complexity and lack of structured metadata.
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 single parameter 'memory_id' fully documented in the schema as 'The ID of the memory to delete'. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline score when schema 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 action ('Delete') and the resource ('a specific memory'), which provides a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'delete_bucket' or explain what distinguishes a 'memory' from other resources in this system.
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 'update_memory' or 'delete_bucket', nor does it mention prerequisites such as needing the memory ID or whether deletion is reversible. It simply states what the tool does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_all_memoriesC
Retrieve all memories with pagination support
| Name | Required | Description | Default |
|---|---|---|---|
| all | No | Get all memories (overrides pagination, default: false) | |
| bucket_id | No | Filter to specific bucket | |
| date_sort | No | Sort results by date (default: false) | |
| include_reference_data | No | Include reference data in response (default: false) | |
| limit | No | Maximum number of results to return (1-100, default: 10) | |
| offset | No | Number of results to skip for pagination (default: 0) | |
| sort_order | No | Sort order (default: 'desc') | |
| source_type | No | Filter by source type |
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 mentions 'pagination support' but doesn't explain how pagination works (e.g., using limit/offset parameters), what the response format looks like, or any constraints like rate limits or permissions required. This leaves significant gaps for a tool with 8 parameters and no output schema.
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 states the core functionality without unnecessary words. It's appropriately sized and front-loaded with the main purpose, 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?
For a tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain the return format, error conditions, or how to interpret results. While the schema covers parameters, the overall context for using this tool effectively is missing, especially given multiple sibling retrieval tools.
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%, so all parameters are documented in the schema. The description adds no additional parameter information beyond mentioning 'pagination support' (which relates to limit/offset), but doesn't explain other parameters like bucket_id or source_type. 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 ('Retrieve') and resource ('all memories'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'get_bucket_memories' or 'search_memories' beyond mentioning pagination support, which is a feature rather than a differentiator.
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 'get_bucket_memories' or 'search_memories'. It mentions pagination support but doesn't explain when this is preferable over other retrieval methods, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_bucket_memoriesC
Get memories from a specific bucket
| Name | Required | Description | Default |
|---|---|---|---|
| bucket_id | Yes | The bucket to retrieve memories from | |
| include_reference_data | No | Include reference data in response (default: false) | |
| limit | No | Maximum number of results to return (1-100, default: 10) | |
| offset | No | Number of results to skip for pagination (default: 0) |
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 retrieves memories but doesn't mention whether this is a read-only operation, if it requires authentication, any rate limits, error conditions, or the format of the returned memories. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
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 directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. There's no wasted verbiage or structural complexity.
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 lack of annotations and output schema, the description is incomplete for a tool with 4 parameters and no structured behavioral hints. It doesn't explain what 'memories' are in this context, how they're returned, or any operational constraints. For a retrieval tool in a system with multiple memory-related siblings, more context is needed to ensure proper use.
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 100% description coverage, clearly documenting all four parameters (bucket_id, include_reference_data, limit, offset) with their purposes, types, defaults, and constraints. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline for adequate but not enhanced semantic value.
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 ('Get') and resource ('memories from a specific bucket'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'get_all_memories' or 'search_memories', which would require more specificity about scope or filtering capabilities.
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 'get_all_memories' (which might retrieve memories across all buckets) or 'search_memories' (which might offer more flexible querying). There's no mention of prerequisites, such as needing an existing bucket, or exclusions for when other tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_bucketsB
Retrieve a list of all available buckets
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 the tool retrieves a list, implying a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns paginated results, or what format the list takes. This is inadequate for a tool with zero 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 that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, earning a perfect score for conciseness.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the returned list contains (e.g., bucket names, IDs, metadata), how it's structured, or any prerequisites for use. For a tool with no structured data to rely on, this leaves significant gaps for an 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?
The tool has zero parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for a parameterless tool.
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 verb ('Retrieve') and resource ('list of all available buckets'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'get_bucket_memories' or 'get_all_memories', which prevents a perfect score.
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. With siblings like 'get_bucket_memories' (for memories in a specific bucket) and 'get_all_memories' (for all memories regardless of bucket), the agent lacks context for choosing appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_usage_statsB
Retrieve user usage statistics and plan information
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states 'retrieve,' implying a read-only operation, but doesn't specify permissions, rate limits, data freshness, or response format. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 directly states the tool's purpose without any fluff or unnecessary elaboration. It's appropriately sized and front-loaded, making it easy for an agent 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 the lack of annotations and output schema, the description is incomplete for a retrieval tool. It doesn't explain what 'usage statistics and plan information' entails, how the data is returned, or any behavioral aspects like error handling. For a tool in a server with multiple siblings, more context is needed to ensure proper usage.
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 tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for such cases is 4, as the description doesn't need to compensate for any parameter gaps, and it correctly avoids redundant 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 verb ('retrieve') and resource ('user usage statistics and plan information'), making the purpose understandable. However, it doesn't differentiate this tool from its siblings (like check_memory_status or get_buckets), which are also retrieval operations but for different resources, so it doesn't reach the highest score.
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. With siblings like get_buckets or get_all_memories that also retrieve data, there's no indication of context, prerequisites, or exclusions, leaving the agent to guess based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
save_memoryC
Save a memory to Memory Box
| Name | Required | Description | Default |
|---|---|---|---|
| bucket_id | No | The bucket to save the memory to (default: "General") | |
| raw_content | No | Raw content for processing (alternative to text) | |
| reference_data | No | Structured metadata for memory storage | |
| source_type | No | Type of memory source (default: 'llm_plugin') | |
| text | Yes | The memory content to save (either text OR raw_content required) |
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. 'Save a memory' implies a write operation, but the description doesn't cover critical aspects like required permissions, whether this creates new or overwrites existing memories, error conditions, or response format. For a mutation tool with complex parameters, this leaves significant gaps in understanding its 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 a single sentence: 'Save a memory to Memory Box'. It's front-loaded and wastes no words, though this brevity contributes to gaps in other dimensions. Every word serves a purpose in stating the 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?
Given the tool's complexity (5 parameters with nested objects, no output schema, and no annotations), the description is insufficient. It doesn't explain what constitutes a 'memory', how saving interacts with the system, what happens on success/failure, or how it relates to sibling tools. For a write operation with rich input structure, more context is needed to use it effectively.
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%, providing detailed documentation for all parameters. The description adds no parameter information beyond what's in the schema, so it doesn't enhance understanding of semantics. However, with comprehensive schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
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 'Save a memory to Memory Box' clearly states the action (save) and resource (memory to Memory Box), but it's quite generic. It doesn't specify what a 'memory' is in this context or differentiate this tool from sibling tools like 'update_memory' or 'create_bucket'. The purpose is understandable but lacks specificity.
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. With siblings like 'update_memory', 'create_bucket', and 'search_memories', the description offers no context on prerequisites, appropriate scenarios, or distinctions. The agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_memoriesB
Search for memories using semantic search
| Name | Required | Description | Default |
|---|---|---|---|
| bucket_id | No | Filter to specific bucket | |
| date_sort | No | Sort semantic search results by date after similarity filtering (default: false) | |
| debug | No | Include debug information in results (default: false) | |
| include_reference_data | No | Include reference data in response (default: false) | |
| limit | No | Maximum number of results to return (1-100, default: 10) | |
| offset | No | Number of results to skip for pagination (default: 0) | |
| query | Yes | The search query (semantic search) | |
| sort_order | No | Sort order when date_sort is enabled (default: 'desc') | |
| source_type | No | Filter by source type |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral information. It mentions 'semantic search' which implies meaning-based rather than keyword-based matching, but doesn't disclose pagination behavior (implied by offset/limit), rate limits, authentication requirements, or what constitutes a 'memory' resource. The description adds some context about the search method but leaves critical behavioral traits unspecified.
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 states the core functionality without unnecessary words. It's appropriately sized for a search tool and front-loads the essential information. Every word earns its place in conveying 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?
Given the complexity (9 parameters, semantic search functionality) and absence of both annotations and output schema, the description is minimally complete. It identifies the core operation but lacks context about what 'memories' are, how results are structured, or performance characteristics. The schema handles parameter documentation well, but the description doesn't compensate for missing behavioral and output context.
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 9 parameters thoroughly with descriptions, constraints, and defaults. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate parameter documentation entirely through the schema, with no additional value from the description.
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 verb ('search') and resource ('memories') with the specific method 'semantic search'. It distinguishes from siblings like 'get_all_memories' (which presumably retrieves all without search) and 'get_related_memories' (which likely finds related items rather than semantic search). However, it doesn't explicitly differentiate from potential text-based search tools if they existed.
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. It doesn't mention when semantic search is appropriate compared to other search methods, nor does it reference sibling tools like 'get_all_memories' or 'get_related_memories' for context. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_memoryC
Update an existing memory including text, bucket, and relationships
| Name | Required | Description | Default |
|---|---|---|---|
| bucket_id | No | Move memory to different bucket | |
| memory_id | Yes | The ID of the memory to update | |
| raw_content | No | New raw content for the memory | |
| reference_data | No | Updated reference data (same structure as save_memory) | |
| source_type | No | Update source type | |
| text | No | New text content for the memory |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states 'Update an existing memory' which implies mutation, but doesn't disclose permissions needed, whether changes are reversible, rate limits, or what happens to unspecified fields. The mention of 'relationships' hints at complexity but lacks operational details.
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?
Extremely concise single sentence with zero waste. Front-loaded with the core action ('Update an existing memory') followed by specific updatable elements. Every word contributes directly to understanding the tool's function.
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 mutation tool with 6 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'relationships' entail in 'reference_data', doesn't mention the required 'memory_id', and provides no information about return values or error conditions. The schema handles parameter documentation, but behavioral context is severely lacking.
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 parameters are well-documented in the schema. The description adds marginal value by listing 'text, bucket, and relationships' which correspond to 'text', 'bucket_id', and 'reference_data' parameters, but doesn't provide additional syntax, format, or constraint details beyond what's in the schema descriptions.
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 ('Update') and resource ('an existing memory'), specifying what can be updated ('including text, bucket, and relationships'). It distinguishes from siblings like 'save_memory' (create) and 'delete_memory' (remove), but doesn't explicitly contrast with all alternatives.
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 on when to use this tool versus alternatives like 'save_memory' for creating new memories or 'delete_memory' for removal. It mentions updating 'relationships' but doesn't clarify when that's needed versus other tools. No explicit when-not-to-use or prerequisite information is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose with no significant overlap. Tools target specific actions on distinct resources like memories, buckets, or system status, making it easy for an agent to select the right one. For example, get_all_memories vs. get_bucket_memories vs. search_memories are well-differentiated by scope and method.
All tool names follow a consistent verb_noun pattern using snake_case, such as create_bucket, delete_memory, and get_usage_stats. This uniformity makes the set predictable and easy to parse, with no deviations in style or convention across the 12 tools.
With 12 tools, the server is well-scoped for a memory management system, covering core operations without bloat. Each tool earns its place by addressing key needs like CRUD for memories and buckets, organization, search, and system monitoring, fitting typical expectations for such a domain.
The tool surface provides complete CRUD and lifecycle coverage for memories and buckets, including create, read, update, delete, search, and organization features. No obvious gaps exist; agents can perform all essential workflows from saving and retrieving memories to managing buckets and checking status.
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