模型上下文协议 (MCP) 服务器提供用于存储、检索和管理保存到本地文件的记忆的工具。
使用可选标签存储文本记忆
通过文本或标签搜索记忆
检索所有存储的记忆
删除特定记忆
查看标签使用情况统计信息
持久存储在本地 JSON 文件中
Related MCP server: Claude Continuity MCP Server
克隆此存储库
安装依赖项：
npm install构建服务器：
npm run build使用以下命令运行服务器：
npm start -- [storage-path]其中storage-path是您想要存储记忆文件的目录（默认为当前目录）。
要将此服务器与 Claude for Desktop 一起使用：
打开您的 Claude for Desktop 配置文件：
macOS： ~/Library/Application Support/Claude/claude_desktop_config.json
Windows： %APPDATA%\Claude\claude_desktop_config.json
添加服务器配置：
{
"mcpServers": {
"memory": {
"command": "node",
"args": [
"/path/to/mcp-memory-server/build/index.js",
"/path/to/storage/directory"
]
}
}
}重启 Claude 桌面版。您可能需要强制退出所有正在运行的 Claude 进程，以使更改生效。
该服务器公开以下工具：
store-memory存储新的记忆。
参数：
content （字符串，必需）：要存储的文本内容
tags （字符串数组，可选）：用于对内存进行分类的标签
search-memories尋找記憶。
参数：
searchTerm （字符串，可选）：要在内存内容中搜索的文本
tag （字符串，可选）：通过此标签过滤记忆
get-all-memories检索所有存储的记忆。
参数：无
delete-memory根据 ID 删除记忆。
参数：
id （字符串，必需）：要删除的内存的 ID
get-tags-summary获取所有已用标签的摘要。
参数：无
以下是一些与 Claude 一起使用的示例查询：
“存储此信息：项目截止日期为2025年3月15日。标签：工作，截止日期”
“我有哪些与工作相关的记忆？”
“让我看看我所有的记忆”
“删除ID为abc123的内存”
“我的记忆里用过哪些标签？”
服务器将记忆存储在指定存储目录中名为memories.json的文件中。
麻省理工学院
Add new observations to existing entities in the knowledge graph
| Name | Required | Description | Default |
|---|---|---|---|
| observations | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits on its own. It only states that it adds observations, without detailing whether observations are appended or replaced, what happens if the entity does not exist (e.g., error or auto-creation), or any other side effects. The tool is clearly a write operation, but critical safety and behavior information is missing.
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 immediately conveys the tool's core function. There is no fluff or redundant phrasing, and the primary verb and object are front-loaded. It earns a high score for efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations and no output schema, the description must provide comprehensive context on its own. However, it only gives a high-level statement and lacks necessary details about input requirements, validation, error handling, or effect on existing data. This leaves significant gaps in the agent's understanding of the tool's full behavior.
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 has zero coverage for the top-level parameter, and the description does not compensate by explaining the parameter structure. Although the nested schema properties describe entityName and contents, the description adds no semantic value beyond the schema, and the agent must rely solely on the schema to understand that observations is an array of objects with those fields. This is insufficient given the low 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 uses a specific verb 'Add' and identifies the resource 'observations' and the target 'existing entities' within the knowledge graph. This clearly distinguishes it from sibling tools like create_entities (which creates entities) and delete_observations (which removes observations), making the tool's purpose unambiguous.
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 that this tool is used when adding observations to existing entities, but it does not explicitly state when to use it over alternatives or provide any comparison with sibling tools. There is no mention of constraints such as 'only for existing entities' or guidance about creating entities first. Thus, the usage context is implied rather than explicitly outlined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create multiple new entities in the knowledge graph
| Name | Required | Description | Default |
|---|---|---|---|
| entities | Yes |
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 only states that it creates entities, but does not mention potential duplicate handling, overwrite behavior, validation rules, or whether the operation is atomic—information an agent would need for a mutating 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, front-loaded sentence that communicates the core purpose with no filler. It is appropriately concise for a simple tool.
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 absence of annotations, an output schema, and limited schema coverage, the description should offer more context about usage, side effects, or return behavior. It does not, leaving significant gaps for an agent to make assumptions about how the tool behaves.
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's top-level 'entities' parameter has no description (coverage 0%), and the tool description does not explain what constitutes an entity (name, type, observations). The description adds no value beyond the bare phrase 'multiple new entities,' failing to compensate for the schema gap.
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 uses a specific verb ('Create') and a specific resource ('multiple new entities in the knowledge graph'), clearly distinguishing this from sibling tools like create_relations and add_observations. It is explicit and unambiguous.
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 create_relations, add_observations, or any exclusions, leaving the agent to infer selection based solely on the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create multiple new relations between entities in the knowledge graph. Relations should be in active voice
| Name | Required | Description | Default |
|---|---|---|---|
| relations | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states 'create' and offers an active-voice guideline, but does not mention idempotency, validation of from/to entities, behavior on duplicates, or error handling.
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 sentence with a brief second clause. Every word earns its place and the main purpose is front-loaded.
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 or output schema, the description is too sparse. It omits critical details such as whether from/to entities must already exist, how duplicates are handled, and whether creation is atomic for the batch.
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 0% at the top level, so the description needed to compensate. It adds the active-voice guideline but does not explain the structure of the relations array or the meaning of from/to/relationType, which the schema already partially covers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (create), the object (multiple new relations), and the context (knowledge graph). It distinguishes from siblings like delete_relations and create_entities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for creating relations, but it does not explicitly state when to use it versus alternatives or mention any prerequisites. The active-voice guideline is a style note, not usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete multiple entities and their associated relations from the knowledge graph
| Name | Required | Description | Default |
|---|---|---|---|
| entityNames | Yes | An array of entity names to delete |
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 does disclose that associated relations are deleted as part of the operation, which is a useful behavioral detail. However, it does not mention irreversibility, permissions, or whether observations are affected, leaving gaps 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 concise sentence that front-loads the action and scope with no unnecessary words. Every word contributes to understanding 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?
For a simple one-parameter destructive tool with no output schema, the description conveys the core purpose but omits behavioral details such as error handling, atomicity, and return values. Given the lack of annotations, it is moderately complete but has room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage of the parameter 'entityNames' with a description, so the baseline is 3. The tool description does not add any additional parameter semantics beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes multiple entities and their associated relations, using a specific verb ('Delete') and resource ('entities' and 'relations'). It distinguishes itself from sibling tools like delete_relations, which only handle relations, and delete_observations, which handles observations.
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 delete_relations or delete_observations. It does not state any exclusions, prerequisites, or scenarios where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete specific observations from entities in the knowledge graph
| Name | Required | Description | Default |
|---|---|---|---|
| deletions | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states the action without mentioning permanence, side effects, required permissions, or return format. As a mutation tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that directly conveys the core action, with no unnecessary words. It is well-structured and front-loaded.
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?
The tool has a non-trivial parameter schema and no output schema or annotations. The description is too minimal to fully inform usage, lacking details on how to specify deletions and what to expect in response.
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 description does not explain the 'deletions' parameter structure. Schema coverage is 0% for the top-level parameter, and the description adds no semantic meaning beyond the schema's nested field 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 function: deleting specific observations from entities in a knowledge graph. It distinguishes itself from sibling tools like delete_entities and add_observations by specifying the granularity ('specific observations').
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 such as delete_entities or when not to use it. The description lacks any context or conditions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete multiple relations from the knowledge graph
| Name | Required | Description | Default |
|---|---|---|---|
| relations | Yes | An array of relations to delete |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits, but it only says 'Delete' without mentioning consequences such as irreversibility, partial failure handling, or permissions. For a mutating operation, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is direct and front-loaded. Every word earns its place, and there is no redundant information.
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?
The tool is simple with one well-specified parameter and no output schema, so the core purpose is covered. However, behavioral details like error handling, atomicity, or effects on related entities are absent, which leaves some ambiguity 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 input schema fully describes the 'relations' parameter and its nested properties (from, to, relationType), so the description adds little beyond what is already structured. The phrase 'multiple' aligns with the array type but does not provide extra meaning.
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 a specific action ('Delete') on a specific resource ('relations'), and the plural 'multiple relations' distinguishes this from sibling tools like delete_entities and delete_observations. It is concise and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for deleting one or more relations, but it does not explicitly state when to prefer this over alternatives, nor does it mention any exclusions or prerequisites. It provides only minimal contextual guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find entities that are accessed frequently
| Name | Required | Description | Default |
|---|---|---|---|
| entityType | No | Optional: filter by entity type | |
| minAccessCount | Yes | Minimum access count threshold |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'accessed frequently' but doesn't explain what constitutes an 'access', how frequency is measured (e.g., time window), whether this is a read-only operation, what permissions might be required, or what the output format looks like. For a tool with no annotation coverage, this leaves significant behavioral 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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loads the core purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and a simple but vague purpose, the description is incomplete. It doesn't clarify what 'entities' are in this context, how 'accessed' is defined, or what the output looks like (e.g., list of entities with access counts). For a tool with 2 parameters and siblings that might overlap, 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%, so the schema already documents both parameters ('entityType' as an optional filter and 'minAccessCount' as a required threshold). The description adds no additional parameter semantics beyond what's in the schema, such as examples of entity types or context for what 'minAccessCount' values are typical. Baseline 3 is appropriate when 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 'Find entities that are accessed frequently' states the general purpose (finding frequently accessed entities) but lacks specificity about what 'entities' are, what 'accessed' means in this context, or how 'frequently' is defined. It doesn't distinguish this tool from siblings like 'get_stale_entities' or 'search_nodes' which might also find entities based on different criteria.
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_stale_entities' (likely for infrequently accessed entities) and 'search_nodes' (likely for general entity search), there's no indication of when this specific frequency-based tool is appropriate, nor any mention of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find entities that haven't been updated recently
| Name | Required | Description | Default |
|---|---|---|---|
| days | Yes | Number of days to consider an entity stale | |
| entityType | No | Optional: filter by entity type (e.g., 'component', 'service') |
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 'finds' entities, implying a read-only operation, but doesn't clarify if it's safe, what permissions are needed, how results are returned (e.g., pagination, format), or any rate limits. This leaves significant gaps for a tool that likely queries 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 that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action, resource, and key criterion concisely.
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 (querying entities based on update recency) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'entities' refer to in this context, how results are structured, or any behavioral traits like safety or performance. 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?
Schema description coverage is 100%, so the input schema fully documents both parameters ('days' and 'entityType'). The description adds no additional meaning beyond implying the 'days' parameter defines 'recently', which is already covered in the schema. 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 tool's purpose with a specific verb ('find') and resource ('entities'), and specifies a key criterion ('that haven't been updated recently'). However, it doesn't explicitly differentiate from sibling tools like 'get_frequently_used' or 'search_nodes', which might also retrieve entities based on different criteria.
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 sibling tools like 'get_frequently_used' for active entities or 'search_nodes' for general searches, nor does it specify prerequisites or exclusions. The usage context is implied but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Open specific nodes in the knowledge graph by their names
| Name | Required | Description | Default |
|---|---|---|---|
| names | Yes | An array of entity names to retrieve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It only says 'open', which implies read-only retrieval, but does not explicitly state that it is non-mutating, what it returns, or how missing names are handled. This leaves significant ambiguity.
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, efficiently worded sentence that directly states the action and resource. It contains no filler or redundant information.
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?
The tool has no output schema and the description does not clarify what 'open' returns (e.g., node attributes, observations, relations). For an agent to invoke the tool and interpret results correctly, this missing information is a notable gap.
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% coverage, already describing 'names' as 'An array of entity names to retrieve'. The tool description merely restates 'by their names', adding no extra semantic detail beyond the schema, so the baseline of 3 applies.
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 uses the specific verb 'Open' and identifies the resource 'nodes in the knowledge graph', scoped by 'names', making it clear this is a direct retrieval by exact names. It implicitly differentiates from search_nodes (searching) and read_graph (full graph), but does not explicitly name 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 is given on when to use this tool versus siblings. The description does not state that it should be used when exact node names are known, nor does it exclude using search_nodes for lookup or read_graph for broader context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the entire knowledge graph
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden of behavioral disclosure, but it only states that the graph is read. It does not mention that the operation is read-only, whether it requires permissions, or that the response may be very large. The word 'read' implies non-destructive behavior, but no details are given.
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 with no redundancy. It is well-structured and every word contributes to the meaning.
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 should explain what reading the graph returns or any caveats (e.g., large payloads). It does not, and it also fails to differentiate this tool from search_nodes for partial reads. The tool is simple, but the description is still incomplete for an agent to use it confidently.
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 schema is empty, so there is nothing to document. The baseline for zero parameters is 4, and the description correctly indicates that no inputs are needed, without adding unnecessary detail.
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 uses a specific verb 'read' and identifies the resource 'the entire knowledge graph,' clearly distinguishing it from sibling tools that create or delete entities. However, it is brief and doesn't elaborate on the output format or how it differs from export_to_obsidian, 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 explicit guidance on when to use this tool over alternatives like search_nodes or open_nodes. It is only implied that this is for reading the whole graph, with no mention of filtering or use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for nodes in the knowledge graph based on a query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query to match against entity names, types, and observation content |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It describes the action as 'search' but does not disclose key behaviors such as case sensitivity, partial matching, result limits, ordering, or whether it searches across all entity fields or just specific ones.
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 sentence with no wasted words, but it is vague and lacks structure. It could be improved by adding brief details or examples without increasing length significantly.
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 simple one-parameter tool without an output schema, the description is minimally adequate. However, given the presence of sibling tools with overlapping functionality, more context (e.g., search scope, result format) would make it complete.
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 covers 100% of the parameter with a description that explains what the query matches against. The tool description restates 'based on a query' but adds no additional semantics beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for nodes in the knowledge graph based on a query, which is specific enough to distinguish from siblings like 'traverse_graph' or 'query_by_time'. However, it could be more precise (e.g., specifying it's a full-text search).
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 (e.g., 'query_by_time', 'traverse_graph', 'read_graph'). No exclusions or context for selection are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an existing entity, preserving previous observations
| Name | Required | Description | Default |
|---|---|---|---|
| metadata | No | Partial metadata to update (optional) | |
| name | Yes | Name of the entity to update | |
| observations | No | New observations (optional) |
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 'preserving previous observations' which adds useful context about data retention, but doesn't cover other critical aspects: whether this requires specific permissions, if updates are reversible, what happens to metadata not mentioned, error conditions, or response format. For a mutation tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently communicates the core action and a key behavioral constraint. Every word earns its place with no redundancy or unnecessary elaboration. It's front-loaded with the primary 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?
For a mutation tool with 3 parameters, no annotations, and no output schema, the description is incomplete. While concise, it lacks critical information about permissions, error handling, response format, and detailed behavioral expectations. The 'preserving previous observations' hint is helpful but insufficient for a tool that modifies data with multiple optional parameters.
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 three parameters (metadata, name, observations) with their types and optionality. The description adds minimal value beyond the schema - it implies observations are preserved rather than replaced, but doesn't clarify parameter interactions or provide format examples. 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 verb 'Update' and resource 'entity', specifying it updates an existing entity. It distinguishes from sibling 'create_entities' by focusing on updates rather than creation, but doesn't explicitly differentiate from other update-like tools like 'add_observations' or 'delete_entities'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'preserving previous observations' which hints at behavior but doesn't specify when to choose this over 'add_observations' for observation management or 'create_entities' for new entities. No explicit when/when-not rules or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate component props to prevent hallucination. Checks if props exist in @props observations
| Name | Required | Description | Default |
|---|---|---|---|
| componentName | Yes | Name of the component to validate | |
| propsToCheck | Yes | Array of prop names to validate |
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 validates props to prevent hallucination and checks existence in '@props observations,' but doesn't describe what happens during validation (e.g., returns success/failure, error messages), whether it's read-only or has side effects, or any constraints like rate limits. This is a significant gap for a validation 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 extremely concise and front-loaded, consisting of two sentences that directly state the purpose and method. Every sentence earns its place by clarifying the tool's function without unnecessary details, making it efficient and easy to understand.
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 (validation with 2 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., validation results, errors), behavioral traits, or usage context. This leaves gaps for an AI agent to understand how to interpret results or handle failures.
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 clear descriptions for both parameters: 'componentName' and 'propsToCheck.' The description adds minimal value beyond the schema by implying the validation context ('@props observations'), but doesn't provide additional syntax, format details, or examples. 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 tool's purpose: 'Validate component props to prevent hallucination. Checks if props exist in @props observations.' It specifies the verb 'validate' and the resource 'component props,' with a clear goal of preventing hallucination. However, it doesn't explicitly differentiate from sibling tools like 'verify_graph_integrity' or 'search_nodes,' which might involve validation or checking, so it lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions checking props in '@props observations,' but doesn't specify context, prerequisites, or exclusions. For example, it doesn't clarify if this should be used before creating entities or as a standalone check, leaving usage ambiguous with siblings like 'verify_graph_integrity.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify the integrity of the knowledge graph by checking for orphaned entities in relationships. Returns hallucinated entity names with fuzzy search suggestions.
| Name | Required | Description | Default |
|---|---|---|---|
| maxSuggestions | No | Maximum number of similar entity suggestions to return for each orphaned entity (default: 3) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the tool's behavior: it performs verification, identifies orphaned entities, and returns hallucinated names with fuzzy search suggestions. However, it doesn't mention performance characteristics (e.g., time-intensive), side effects, or error handling, leaving gaps for a tool with potential complexity.
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 output. Every word contributes value without redundancy, making it easy to parse and understand 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 provides basic behavioral context but lacks details on return format (e.g., structure of suggestions), error cases, or operational constraints. It's adequate for a simple tool but could be more complete for integrity verification in a knowledge graph 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 the single parameter 'maxSuggestions' with its purpose and default. The description doesn't add any parameter-specific information beyond what the schema provides, meeting the baseline for high 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 tool's purpose with specific verbs ('verify integrity', 'check for orphaned entities') and resources ('knowledge graph', 'relationships'). It distinguishes from siblings like 'validate_component_props' or 'get_stale_entities' by focusing on relationship integrity rather than component validation or staleness detection.
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 explicit guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing (e.g., after deletions), or compare to siblings like 'validate_component_props' for other integrity checks. The description implies usage for finding orphaned entities but lacks context on when this is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0add_observationscreate_entitiescreate_relationsdelete_entitiesdelete_observationsdelete_relationsget_frequently_usedget_stale_entitiesopen_nodesread_graphsearch_nodesupdate_entityvalidate_component_propsverify_graph_integrityScored across 14 tools
Most tools have distinct purposes targeting specific operations on entities, relations, or observations, with clear boundaries. However, 'open_nodes' and 'search_nodes' could be confused as both involve accessing nodes, though 'open_nodes' appears to retrieve by name while 'search_nodes' uses a query.
Tool names follow a consistent verb_noun pattern throughout, such as 'add_observations', 'create_entities', and 'delete_relations'. All tools use snake_case with clear, descriptive verbs, making the set predictable and readable.
With 14 tools, the count is well-scoped for a knowledge graph management server, covering CRUD operations, querying, and maintenance tasks. Each tool earns its place without feeling excessive or insufficient for the domain.
The toolset provides comprehensive coverage for knowledge graph operations, including entity and relation management, observation handling, search, and integrity checks. A minor gap is the lack of a tool to update relations, but agents can work around this by deleting and recreating them.
Persistent memory layer for AI tools. Save and recall notes across Claude and other MCP clients.
Persistent memory for AI assistants. Save once; recall from Claude, ChatGPT, or any MCP client.
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
Private persistent memory for Claude, ChatGPT & Gemini via MCP - semantic search, zero-code setup.
