Stores AI memories as Markdown files for visualization in Obsidian's graph view, creating a knowledge graph with entities as nodes and relations as edges.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Obsidian Memory MCP create a person entity for John Doe who works at Tech Corp"
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.
MCP server that stores AI memories as Markdown files for visualization in Obsidian's graph view.
This project is a modified version of Anthropic's memory server that has been adapted for Obsidian integration. The original server stored memories in JSON format, while this version stores them as individual Markdown files with Obsidian-compatible [[link]] syntax for graph visualization.
Storage Format: Changed from JSON to individual Markdown files
Obsidian Integration: Added [[link]] syntax for relations
YAML Frontmatter: Metadata stored in frontmatter instead of JSON
File Structure: Each entity becomes a separate .md file
Removed Features: Simplified to focus on Markdown-only storage
Related MCP server: Knowledge Graph Memory Server
Markdown Storage: Individual .md files for each entity
Obsidian Integration: Uses [[link]] syntax for graph visualization
Knowledge Graph: Store entities, relations, and observations
Search Functionality: Query across all stored memories
YAML Frontmatter: Metadata stored in frontmatter
Each entity is stored as an individual Markdown file with:
YAML frontmatter for metadata (entityType, created, updated)
Obsidian-compatible [[links]] for relations
Organized sections for observations and relations
Example entity file (John_Doe.md):
---
entityType: person
created: 2025-07-10
updated: 2025-07-10
---
# John Doe
## Observations
- Works at Tech Corp
- Expert in TypeScript
- Lives in Tokyo
## Relations
- [[Manager of::Alice Smith]]
- [[Collaborates with::Bob Johnson]]
- [[Located in::Tokyo Office]]git clone https://github.com/YuNaga224/obsidian-memory-mcp.git
cd obsidian-memory-mcp
npm install
npm run buildThen configure in Claude Desktop:
{
"mcpServers": {
"obsidian-memory": {
"command": "node",
"args": ["/full/path/to/obsidian-memory-mcp/dist/index.js"],
"env": {
"MEMORY_DIR": "/path/to/your/obsidian/vault"
}
}
}
}Configure Claude Desktop with one of the options above
Restart Claude Desktop
Use the MCP memory tools to create entities and relations
Open Obsidian and view the graph
The knowledge graph will be visualized with:
Entity files as nodes
[[links]] as edges
Different colors for different entity types (if configured in Obsidian)
The server exposes the following tools:
create_entities: Create new entities
create_relations: Create relations between entities
add_observations: Add observations to existing entities
delete_entities: Delete entities and related data
delete_observations: Remove specific observations
delete_relations: Remove relations
read_graph: Get the entire knowledge graph
search_nodes: Search entities by query
open_nodes: Get specific entities by name
npm run watch # Watch for changes and rebuildThis project is based on Anthropic's memory server from the Model Context Protocol servers collection. We thank Anthropic for releasing the original implementation under the MIT license.
MIT License - see LICENSE file for details.
Original memory server: Copyright (c) 2024 Anthropic, PBC
Obsidian integration modifications: Copyright (c) 2025 YuNaga224
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
add_observationscreate_entitiescreate_relationsdelete_entitiesdelete_observationsdelete_relationsopen_nodesread_graphsearch_nodesScored across 9 tools
Each tool has a clearly distinct purpose targeting specific operations on the knowledge graph: adding/deleting observations, creating/deleting entities/relations, opening/reading/searching nodes. No overlap exists; for example, 'create_entities' and 'create_relations' are distinct, and 'open_nodes' and 'search_nodes' serve different access functions.
All tools follow a consistent verb_noun pattern with snake_case throughout: 'add_observations', 'create_entities', 'delete_relations', etc. The naming is predictable and readable, with verbs like 'add', 'create', 'delete', 'open', 'read', and 'search' applied consistently to their respective nouns.
With 9 tools, the count is well-scoped for managing a knowledge graph, covering core operations like CRUD for entities, relations, and observations, plus graph access. Each tool earns its place without bloat, fitting a typical range of 3-15 tools for this domain.
The tool set provides strong coverage for knowledge graph management, including create, read, delete, and search operations. A minor gap exists in update capabilities (e.g., no 'update_entities' or 'update_relations'), but agents can work around this by deleting and recreating, and core workflows are well-supported.
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