mcp-structured-memory
The mcp-structured-memory server is a Model Context Protocol (MCP) server that enables AI-assisted, structured memory management for focused projects using markdown files stored locally.
Core Capabilities:
Document Management: Create new structured memory documents, list all available documents, retrieve full documents or summaries, and search within documents
Content Organization: Update entire sections with Markdown formatting (append or replace), organize content into custom sections tailored to specific domains like travel planning or research
List Management: Add items to lists, update existing list items, and move items between sections within documents
AI Integration: Enable dynamic content updates through AI learning from conversations and automated organization
Data Protection: Automatically create timestamped backups before major updates to ensure data integrity
The server facilitates building and maintaining living documents for domain-specific applications, storing everything as standard markdown files in platform-specific local directories.
Supports storage of memory documents on Linux systems, with files stored in ~/.local/share/mcp-structured-memory/.
Supports storage of memory documents on macOS systems, with files stored in ~/Library/Application Support/mcp-structured-memory/.
Uses markdown files as the storage format for memory documents, allowing structured organization of information that can be easily read and updated.
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., "@mcp-structured-memorycreate a new memory document for my research project on renewable energy"
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.
Structured Memory MCP Server
A Model Context Protocol (MCP) server that provides structured, domain-specific memory management through markdown files. This is particularly useful for ongoing projects around a particular area of focus where you want to accumulate valuable context over time. Examples include focused domains like travel planning, research projects, real estate search, investment theses, product planning, and career development.
Why Structured Memory?
Traditional MCP memory servers use semantic search across scattered conversation snippets. This works well for general recall but fails for focused projects that need organized, categorical information.
Structured Memory instead maintains living documents with structured content that you can scan, update, and track over time - just like you would with a personal notebook, but with AI assistance.
Memory documents are stored as markdown files, primarily updated automatically by the LLM as it learns from your conversations to build rich context over time.
Related MCP server: CastPlan MCP
Typical usage
Ask your LLM client to create a memory document for your focused project
Create a new travel advisor memory document and tell me how to use it. This should start empty and grow over time.Create a new Project for conversations in that area. Add the provided usage instructions to your project context
Note: LLMs will, against all tool advice, occasionally fail to show you the installation instructions. If that happens, follow up with a request to the LLM to show you the installation instructions and it should comply.
New chat conversations start by checking the available info from the project's memory. Your LLM will update this memory over time to build up valuable context.
Optionally prompt your LLM to add/adjust the memory file on demand. It's also helpful to ask the LLM to prompt you for relevant info (e.g. "Ask me some questions to learn about my travel preferences")
Installation
Option 1: Install from npm (when published)
npm install -g @nmeierpolys/mcp-structured-memoryOption 2: Install from source
git clone https://github.com/nmeierpolys/mcp-structured-memory.git
cd mcp-structured-memory
npm install
npm run buildConfiguration
For Claude Desktop
Edit the claude_desktop_config.json file with the following entry:
{
"mcpServers": {
"mcp-structured-memory": {
"command": "npx",
"args": ["@nmeierpolys/mcp-structured-memory"]
}
}
}Available Tools
create_memory - Create a new memory document with optional initial content
list_memories - List all available memory documents
get_memory_summary - Get a high-level summary of a memory document
get_section - Retrieve a specific section from a memory document
get_full_memory - Retrieve the complete content of a memory document
search_within_memory - Search for information within a memory document
update_section - Update an entire section of a memory document
add_to_list - Add an item to a list section
update_list_item - Update an existing item in a list
move_list_item - Move an item between list sections
Flexible Structure
Memory documents support any structure you need. The AI will help you organize sections based on your use case. Common examples:
Travel Planning: destinations, itinerary, accommodations, activities, restaurants, travel tips, budget tracker
Research Project: research questions, literature review, methodology, findings, data sources, next steps
Real Estate Search: search criteria, active listings, visited properties, rejected properties, market insights, agent contacts
Example: Automatic Memory Building
User: "Create a new Minnesota trip memory document and tell me how to use it. This should start empty and grow over time."
Claude: "I've created a travel memory document for your Minnesota trip. As we discuss your plans, I'll automatically update it with destinations, timing, preferences, and other details I learn about your trip."
User: "I want to see fall colors, go hiking, try local cuisine, and visit both the North Shore and the Twin Cities."
Claude: "I've noted your priorities in the memory: fall foliage viewing, hiking opportunities, local restaurants, North Shore destinations, and Twin Cities attractions. I'll keep track of specific recommendations as we explore options."
[Later conversation]
User: "What hiking trails have good fall colors?"
Claude: "Based on our previous discussions, I see you're focused on fall colors in Minnesota. Let me suggest some trails and I'll add the best ones to your travel memory..."Backup and Version Control
The server automatically creates timestamped backups before major updates.
Storage Locations
Memory document files are stored as markdown files in:
macOS:
~/Library/Application Support/mcp-structured-memory/Windows:
%LOCALAPPDATA%\mcp-structured-memory\Linux:
~/.local/share/mcp-structured-memory/
Available Tools
10 toolsadd_to_listC
Add an item to a list section in a memory document
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to update | |
| section | Yes | The section name to add the item to | |
| item | Yes | The item data to add (structure depends on template) |
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 implies a mutation ('Add an item') but doesn't specify permissions required, whether the operation is idempotent, or how errors are handled. This leaves significant gaps in understanding the tool's behavior beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, 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 the complexity of a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't cover behavioral aspects like error handling or return values, and while the schema covers parameters, the overall context for safe and effective use is 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?
The input schema has 100% description coverage, so the schema already documents all three parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining the 'item' object structure or 'section' naming conventions, 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 ('Add an item') and target resource ('to a list section in a memory document'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'update_list_item' or 'move_list_item', which might handle similar list operations, so it falls short of 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 such as 'update_list_item' or 'create_memory', nor does it mention prerequisites like needing an existing memory document. It only states what the tool does, leaving usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_memoryA
Create a new structured memory document with optional initial content. IMPORTANT: After using this tool, you MUST show the user the complete installation instructions returned by the tool - the memory will not work without proper MCP server setup and project context configuration.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Unique identifier/name for this memory | |
| content | No | Optional initial content for the memory document. Can be brief context (e.g., 'planning a trip to Japan') or full Markdown content. Focus on capturing what you've learned FROM the user (their preferences, requirements, decisions) rather than generating extensive AI content. |
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: it creates a new document (implying mutation), requires showing installation instructions post-invocation, and hints at setup dependencies ('MCP server setup and project context configuration'). However, it doesn't cover aspects like error conditions, permissions needed, or what happens if the name isn't unique, 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 appropriately sized with two sentences: the first states the purpose and key parameter detail, and the second provides critical usage instructions. It's front-loaded with the core action and avoids unnecessary elaboration, though the second sentence is somewhat lengthy but justified by its importance.
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 (a creation tool with setup dependencies), no annotations, and no output schema, the description does a good job by explaining the creation action and post-invocation requirements. However, it lacks details on the return value (e.g., what 'installation instructions' entail) and doesn't address potential errors or uniqueness constraints, which could be improved for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already fully documents the two parameters (name and content). The description adds minimal semantic value beyond the schema—it mentions 'optional initial content' and provides an example ('e.g., planning a trip to Japan'), but this is largely redundant with the schema's descriptions. 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 ('Create a new structured memory document') and resource ('memory document'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'update_section' or 'add_to_list' that might also create or modify memory content, 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 explicit usage guidance with 'IMPORTANT: After using this tool, you MUST show the user the complete installation instructions returned by the tool - the memory will not work without proper MCP server setup and project context configuration.' This clearly indicates when and how to use this tool, including critical post-invocation steps, which is comprehensive guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_full_memoryA
Retrieve the complete content of a memory document with all Markdown formatting preserved (headings, bold, italic, code, links, tables, lists, etc.)
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to retrieve |
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 discloses the tool's behavior by stating it preserves Markdown formatting, which is useful context beyond basic retrieval. However, it lacks details on error handling, permissions, or response format, leaving gaps for a mutation-free but context-sensitive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and includes essential details (Markdown formatting) without waste. Every part earns its place by clarifying the tool's scope and output characteristics.
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 low complexity (single parameter, no output schema, no annotations), the description is reasonably complete for a read-only retrieval operation. It covers the purpose and output formatting well, but could benefit from mentioning error cases or linking to sibling tools for better contextual integration.
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 the 'memory_id' parameter fully. The description does not add any meaning beyond the schema, such as format examples or constraints, but it does not need to compensate for gaps. Baseline 3 is appropriate as the schema handles 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 clearly states the specific action ('Retrieve the complete content') and resource ('memory document') with explicit scope ('all Markdown formatting preserved'). It distinguishes from siblings like 'get_memory_summary' by emphasizing completeness versus summarization.
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 context by specifying 'complete content' with formatting preserved, suggesting it should be used when full fidelity is needed versus summaries or sections. However, it does not explicitly name alternatives or state when not to use it, such as comparing to 'get_section' or 'search_within_memory'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_memory_summaryC
Get a high-level summary of a memory document
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to summarize |
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 states the tool retrieves a 'high-level summary' but doesn't disclose behavioral traits such as what format the summary is in, if it's read-only (implied but not explicit), any rate limits, or error conditions. This leaves significant gaps for an agent to understand how the tool behaves.
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 unnecessary words. It's appropriately sized and front-loaded, 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 no annotations and no output schema, the description is incomplete. It doesn't explain what the summary includes (e.g., key points, metadata), how it's structured, or any limitations. For a tool with one parameter but unknown output behavior, more context is needed to guide 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?
Schema description coverage is 100%, with the single parameter 'memory_id' clearly documented in the schema. The description doesn't add any meaning beyond this, such as explaining what constitutes a valid memory ID or how to obtain one. Baseline 3 is appropriate since the schema handles 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 clearly states the action ('Get') and resource ('a high-level summary of a memory document'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_full_memory' or 'get_section', which also retrieve memory-related information but with different scope or detail.
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 'get_full_memory' (likely retrieves complete content) and 'get_section' (likely retrieves specific parts), the description lacks context on choosing this tool for a summary over other retrieval options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sectionC
Retrieve a specific section from a memory document
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to read from | |
| section | Yes | The section name to retrieve |
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 'Retrieve,' implying a read-only operation, but doesn't specify permissions required, error handling (e.g., if the section doesn't exist), or performance aspects like response format or potential latency. This leaves significant gaps for a tool that accesses 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, direct sentence that efficiently conveys the core action without unnecessary words. It is front-loaded with the main purpose, making it easy to parse quickly, and every part of the sentence contributes 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?
Given the complexity of retrieving data from a document, the lack of annotations and output schema means the description should do more to be complete. It doesn't explain what the retrieved section contains (e.g., text, metadata), error conditions, or how it relates to sibling tools, leaving the agent with insufficient context for reliable 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 both parameters ('memory_id' and 'section') with their types and purposes. The description adds no additional parameter details beyond what the schema provides, such as examples or constraints, so it meets the baseline for high schema coverage without compensating 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 ('Retrieve') and resource ('a specific section from a memory document'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_full_memory' or 'get_memory_summary', which likely retrieve broader content from memory documents.
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, such as needing an existing memory document, or compare it to siblings like 'get_full_memory' for retrieving entire documents or 'search_within_memory' for finding sections based on content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_memoriesB
List all available memory documents
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 it lists 'all available memory documents' but doesn't clarify aspects like pagination, ordering, or what 'available' means (e.g., permissions, filters). This leaves significant gaps for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, 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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but incomplete. It lacks details on behavioral traits like return format or limitations, which are needed for full context despite the low complexity.
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 no parameter information is needed. The description doesn't add param semantics, but this is appropriate, earning a baseline score of 4 for zero-param tools.
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 ('List') and resource ('memory documents'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'search_within_memory' or 'get_memory_summary', which prevents a score of 5.
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 such as 'search_within_memory' or 'get_memory_summary'. The description lacks context about usage scenarios or exclusions, offering minimal help for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
move_list_itemC
Move an item from one section to another
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to update | |
| from_section | Yes | The source section containing the item | |
| to_section | Yes | The destination section for the item | |
| item_identifier | Yes | Identifier for the item to move (e.g., company name) | |
| reason | No | Optional reason for the move (stored as metadata) |
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 states the action is a 'move' which implies mutation, but doesn't disclose behavioral traits like whether this requires specific permissions, if it's destructive (e.g., removes from source), what happens if sections don't exist, or any rate limits. The description is minimal and lacks important operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 7 words, front-loaded with the core action. Every word earns its place - 'move' specifies the action, 'item' specifies what's being moved, and 'from one section to another' specifies the scope. No wasted words 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?
For a mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens during the move operation, what the expected outcome is, whether there are side effects, or what format the response takes. The 100% schema coverage helps with parameters, but the overall operational context is missing.
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 5 parameters thoroughly. The description doesn't add any meaningful semantic context beyond what's in the schema - it doesn't explain relationships between parameters, provide examples, or clarify edge cases. Baseline 3 is appropriate when 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 ('move') and resource ('item from one section to another'), providing a specific verb+resource combination. However, it doesn't differentiate this tool from sibling tools like 'update_list_item' or 'update_section', which might have overlapping functionality for list/section modifications.
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 'update_list_item' and 'update_section' that might handle similar operations, there's no indication of when this specific move operation is appropriate versus other modification tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_within_memoryC
Search for information within a memory document
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to search | |
| query | Yes | The search query (words or phrases) |
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 the tool searches within a memory document but does not describe how the search works (e.g., keyword matching, relevance ranking), what the output looks like (e.g., snippets, full text), or any limitations (e.g., performance, access controls). For a search tool with zero annotation coverage, this 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 directly states the tool's function without unnecessary words. It is front-loaded with the core action ('Search for information') and resource ('within a memory document'), making it easy to parse. Every part of the sentence earns its place by conveying essential information succinctly.
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 a search operation, the lack of annotations, and no output schema, the description is incomplete. It does not explain what the search returns, how results are formatted, or any behavioral aspects like error handling. For a tool with two parameters and no structured output, more context is needed to understand its full functionality and limitations.
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 clear documentation for 'memory_id' and 'query'. The description adds no additional meaning beyond what the schema provides, such as examples of queries or details on memory ID formats. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Search for information within a memory document', which is clear but vague. It specifies the verb ('search') and resource ('memory document'), but does not distinguish it from siblings like 'get_full_memory' or 'get_memory_summary', which might also retrieve memory content. The purpose is understandable but lacks specificity about what type of search or information is involved.
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 does not mention prerequisites, such as needing an existing memory document, or compare it to siblings like 'get_full_memory' for broader retrieval or 'list_memories' for overviews. Without this context, users must infer usage from the tool name alone, which is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_list_itemC
Update an existing item in a list section
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to update | |
| section | Yes | The section containing the item to update | |
| item_identifier | Yes | Identifier for the item to update (e.g., company name, contact name) | |
| updates | Yes | Fields to update with their new values |
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. It states 'Update an existing item' implying a mutation, but doesn't cover critical aspects like required permissions, whether updates are reversible, error handling (e.g., if item doesn't exist), or rate limits. This leaves significant gaps for a tool that modifies 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, efficient sentence with no wasted words. It's front-loaded with the core action and target, making it easy to parse quickly. Every word earns its place without redundancy.
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 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., side effects, error cases), output format, or how it differs from sibling tools. The agent must rely heavily on schema alone, which is insufficient for safe invocation.
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 fully documents all 4 parameters. The description adds no additional meaning beyond implying the tool operates on list items, which is already clear from parameter names like 'section' and 'item_identifier'. 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 ('Update') and target ('an existing item in a list section'), which is specific and actionable. However, it doesn't distinguish this tool from sibling tools like 'update_section' or 'move_list_item', which also modify list content, leaving some ambiguity about when to choose one over the other.
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., needing an existing item), exclusions, or comparisons to siblings like 'add_to_list' (for new items) or 'update_section' (for section-level changes), leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_sectionC
Update an entire section of a memory document. Content supports full Markdown formatting including headings, bold, italic, code blocks, links, lists, tables, and all standard Markdown syntax.
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes | The ID of the memory document to update | |
| section | Yes | The section name to update | |
| content | Yes | The new content for the section. Supports full Markdown: headings (#), **bold**, *italic*, `code`, [links](url), ```code blocks```, lists, tables, etc. | |
| mode | No | Whether to append to or replace the section content (default: append) |
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. It mentions Markdown formatting support but fails to describe critical behaviors: whether this operation requires specific permissions, if it's idempotent, what happens on invalid section names, error responses, or rate limits. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 sized with two sentences that efficiently convey the core functionality and Markdown support. It's front-loaded with the main purpose. The Markdown examples could be slightly more concise, but overall there's minimal wasted text.
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 4 parameters, no annotations, and no output schema, the description is incomplete. It covers the basic operation and formatting but lacks crucial context about permissions, error handling, side effects, and what the tool returns. The absence of output schema means the description should ideally mention return values, but it doesn't.
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 repeating Markdown support information that's already in the 'content' parameter schema. It doesn't provide additional context about parameter interactions, constraints, or examples beyond what's in the structured 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 verb 'Update' and the resource 'an entire section of a memory document', making the purpose explicit. It distinguishes from siblings like 'update_list_item' by specifying it works on document sections rather than list items. However, it doesn't explicitly contrast with 'create_memory' or 'get_section', leaving some sibling differentiation incomplete.
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 'create_memory' for new documents, 'get_section' for reading, or 'update_list_item' for list operations. It mentions Markdown support but doesn't specify prerequisites, error conditions, or when 'append' vs 'replace' mode is appropriate beyond the schema's default.
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. Dates show when Glama detected each change.
10 tool updates
- First observed
add_to_list - First observed
create_memory - First observed
get_full_memory - First observed
get_memory_summary - First observed
get_section - First observed
list_memories - First observed
move_list_item - First observed
search_within_memory - First observed
update_list_item - First observed
update_section
TDQS
Each tool has a clearly distinct purpose targeting specific operations on structured memory documents. For example, add_to_list, move_list_item, and update_list_item are all focused on list management but handle different actions (adding, moving, updating), while get_full_memory, get_memory_summary, and get_section provide different levels of retrieval detail. There is no ambiguity in tool selection.
All tools follow a consistent verb_noun naming pattern (e.g., create_memory, list_memories, update_section). The verbs are clear and descriptive, and there are no deviations in style or convention throughout the set, making it predictable and easy to understand.
With 10 tools, the count is well-scoped for managing structured memory documents. Each tool serves a specific function in the lifecycle (create, retrieve, update, search, list), and none feel redundant or unnecessary, fitting the domain appropriately without being overwhelming.
The tool set provides complete coverage for the structured memory domain, including creation (create_memory), listing (list_memories), retrieval at various levels (get_full_memory, get_memory_summary, get_section), updating (update_section, update_list_item), list management (add_to_list, move_list_item), and search (search_within_memory). There are no obvious gaps, and the tools support full CRUD and lifecycle operations.
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