mcp-memory-libsql
mcp-메모리-libsql
libSQL 기반의 모델 컨텍스트 프로토콜(MCP)을 위한 고성능 영구 메모리 시스템입니다. 이 서버는 libSQL을 백업 저장소로 사용하여 벡터 검색 기능과 효율적인 지식 저장 기능을 제공합니다.
특징
🚀 libSQL을 사용한 고성능 벡터 검색
💾 엔티티 및 관계의 영구 저장
🔍 의미 검색 기능
🔄 지식 그래프 관리
🌐 로컬 및 원격 libSQL 데이터베이스와 호환
🔒 원격 데이터베이스를 위한 보안 토큰 기반 인증
Related MCP server: dragon-brain
구성
이 서버는 MCP 구성의 일부로 사용하도록 설계되었습니다. 다음은 다양한 환경에 대한 예시입니다.
클라인 구성
Cline MCP 설정에 다음을 추가하세요.
지엑스피1
WSL 구성을 사용한 Claude Desktop
WSL에서 Claude Desktop을 사용하여 이 서버를 설정하는 방법에 대한 자세한 가이드는 WSL에서 Claude Desktop을 사용하여 MCP 서버 작동시키기를 참조하세요.
WSL 환경에 대한 Claude Desktop 구성에 다음을 추가하세요.
{
"mcpServers": {
"mcp-memory-libsql": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"source ~/.nvm/nvm.sh && LIBSQL_URL=file:/path/to/database.db /home/username/.nvm/versions/node/v20.12.1/bin/npx mcp-memory-libsql"
]
}
}
}데이터베이스 구성
서버는 LIBSQL_URL 환경 변수를 통해 로컬 SQLite와 원격 libSQL 데이터베이스를 모두 지원합니다.
로컬 SQLite 데이터베이스의 경우:
{
"env": {
"LIBSQL_URL": "file:/path/to/database.db"
}
}원격 libSQL 데이터베이스(예: Turso)의 경우:
{
"env": {
"LIBSQL_URL": "libsql://your-database.turso.io",
"LIBSQL_AUTH_TOKEN": "your-auth-token"
}
}참고: WSL을 사용할 때 데이터베이스 경로가 Windows 형식이 아닌 Linux 파일 시스템 형식(예: /home/username/... )을 사용하는지 확인하세요.
기본적으로 URL이 제공되지 않으면 현재 디렉토리의 file:/memory-tool.db 사용합니다.
API
서버는 추가적인 벡터 검색 기능을 갖춘 표준 MCP 메모리 인터페이스를 구현합니다.
엔티티 관리
임베딩을 사용하여 엔터티 생성/업데이트
엔터티 삭제
유사성으로 엔터티 검색
관계 관리
엔터티 간 관계 생성
관계 삭제
관련 엔터티 쿼리
건축학
서버는 다음 스키마를 가진 libSQL 데이터베이스를 사용합니다.
엔터티 테이블: 엔터티 정보 및 임베딩을 저장합니다.
관계 테이블: 엔터티 간의 관계를 저장합니다.
libSQL의 내장 벡터 연산을 사용하여 구현된 벡터 검색 기능
개발
출판
npm 2FA 요구 사항으로 인해 게시는 수동으로 수행해야 합니다.
변경 세트를 만듭니다(변경 사항을 문서화합니다):
pnpm changeset패키지 버전(버전 및 CHANGELOG 업데이트):
pnpm changeset versionnpm에 게시합니다(2FA 코드를 입력하라는 메시지가 표시됨):
pnpm release기여하다
기여를 환영합니다! 풀 리퀘스트를 제출하기 전에 기여 지침을 꼭 읽어주세요.
특허
MIT 라이센스 - 자세한 내용은 LICENSE 파일을 참조하세요.
감사의 말
모델 컨텍스트 프로토콜을 기반으로 구축됨
libSQL 로 구동됨
Available Tools
6 toolscreate_entitiesB
Create new entities with observations and optional embeddings
| Name | Required | Description | Default |
|---|---|---|---|
| entities | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'Create new entities' which implies a write/mutation operation, but doesn't disclose behavioral traits like permissions needed, whether it's idempotent, rate limits, or what happens on failure. The mention of 'optional embeddings' hints at functionality but lacks 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 a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, and every word ('new entities', 'observations', 'optional embeddings') adds value. No extraneous information or repetition.
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, no output schema, and a single but complex parameter (nested array of objects), the description is incomplete. It doesn't explain return values, error handling, or the full scope of entity creation (e.g., validation rules, uniqueness constraints). For a mutation tool with rich nested data, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that entities include 'observations and optional embeddings', which clarifies the purpose of the 'entities' array parameter beyond the schema's structural definition. However, it doesn't detail all nested fields like 'entityType' or 'name', leaving some semantics uncovered.
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 'Create' and the resource 'new entities', specifying they include 'observations and optional embeddings'. It distinguishes from siblings like delete_entity (deletion) and read_graph/search_nodes (read operations), though not explicitly named. The purpose is specific but could more directly contrast with create_relations.
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 is provided. It doesn't mention prerequisites, when not to use it, or compare with siblings like create_relations (for relationships) or search_nodes (for finding existing entities). The description implies usage for entity creation but offers no contextual boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_relationsC
Create relations between entities
| Name | Required | Description | Default |
|---|---|---|---|
| relations | Yes |
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 'Create relations' which implies a write/mutation operation, but doesn't specify permissions needed, whether it's idempotent, error handling, or what happens on success/failure. It lacks details on rate limits, side effects, or response format, leaving significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence 'Create relations between entities'. It's front-loaded and wastes no words, though this brevity contributes to underspecification in other dimensions. Every word serves a purpose, making it efficient in structure.
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 (mutation operation with 1 parameter involving nested objects), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It doesn't cover behavioral aspects, parameter meanings, or expected outcomes, making it inadequate for safe and effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter semantics beyond the input schema. With 0% schema description coverage and 1 parameter ('relations' array of objects with source, target, type), the description doesn't explain what these fields mean, valid values for 'type', or examples. It fails to compensate for the schema's lack of descriptions, leaving parameters poorly documented.
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 'Create relations between entities' clearly states the action (create) and resource (relations between entities), but it's vague about what 'entities' refers to and doesn't distinguish from sibling tools like 'create_entities' or 'delete_relation'. It provides basic purpose but lacks specificity about the domain or system context.
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., entities must exist first), exclusions, or comparisons to siblings like 'create_entities' (for creating entities) or 'delete_relation' (for removing relations). Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_entityA
Delete an entity and all its associated data (observations and relations)
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Name of the entity to delete |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the destructive nature ('Delete... and all its associated data'), which is crucial behavioral context. However, it lacks details on permissions needed, error conditions, or what happens if the entity doesn't exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action and scope. Every word contributes meaning without redundancy, making it appropriately sized for a one-parameter 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?
For a destructive mutation tool with no annotations and no output schema, the description adequately covers the basic action and scope. However, it lacks important context about permissions, confirmation requirements, or return values, leaving gaps for safe agent operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing a clear parameter description. The tool description doesn't add any parameter-specific information beyond what's in the schema, 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 specific action ('Delete') and target resource ('an entity and all its associated data'), with explicit mention of what gets deleted ('observations and relations'). It distinguishes from sibling tools like 'delete_relation' by specifying broader scope.
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 is provided. While the description implies destructive deletion, it doesn't specify prerequisites, warn about irreversible changes, or mention when to choose 'delete_entity' over 'delete_relation' or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_relationC
Delete a specific relation between entities
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Source entity name | |
| target | Yes | Target entity name | |
| type | Yes | Type of relation |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states 'Delete', implying a destructive mutation, but doesn't disclose behavioral traits such as whether deletion is permanent, requires specific permissions, has side effects (e.g., cascading deletions), or what happens on success/failure. This is a significant gap for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core action without unnecessary elaboration, 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 destructive operation with no annotations and no output schema, the description is incomplete. It lacks crucial context like what 'delete' entails (e.g., irreversible), expected outcomes, error handling, or how it differs from sibling tools. For a mutation tool, this minimal description is inadequate.
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 (source, target, type) with basic descriptions. The description adds no additional meaning beyond what the schema provides, such as examples of relation types or how entities are identified. 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 clearly states the action ('Delete') and the resource ('a specific relation between entities'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'delete_entity' or 'create_relations', which would require specifying what makes this tool unique for relation deletion versus entity deletion or relation creation.
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 'delete_entity' and 'create_relations', it's unclear if this is for removing existing relations only, or if there are prerequisites (e.g., the relation must exist). No explicit when/when-not or alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_graphB
Get recent entities and their relations
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only states what the tool does, not behavioral traits like permissions needed, rate limits, or what 'recent' means (e.g., time frame). It doesn't disclose critical operational details 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 with no wasted words. It's front-loaded and appropriately sized 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 no annotations, no output schema, and a simple tool with 0 parameters, the description is incomplete. It lacks details on what 'recent' entails, the format of returned data, or any behavioral constraints, making it insufficient 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?
There are 0 parameters, and schema description coverage is 100%, so no parameter information is needed. The description adds context about retrieving 'recent' entities and relations, which is meaningful beyond the empty 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 'Get' and the resources 'recent entities and their relations', making the purpose understandable. It doesn't explicitly distinguish from siblings like 'search_nodes', but it's not vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'search_nodes' or 'create_entities'. The description implies usage for retrieving recent data but doesn't specify contexts, exclusions, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_nodesC
Search for entities and their relations using text or vector similarity
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the search functionality, it doesn't describe what happens during execution - whether it's read-only or has side effects, what permissions are required, how results are returned, or any rate limits. The description is too minimal for a search tool with no annotation support.
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 directly states the tool's function. There's no wasted language or unnecessary elaboration. It's front-loaded with the core purpose and uses efficient phrasing.
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 search tool with no annotations, no output schema, and minimal parameter documentation, the description is insufficient. It doesn't explain what 'entities and relations' mean in this context, how results are structured, whether there are pagination or filtering options, or any operational constraints. The description leaves too many questions unanswered for effective tool 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 description mentions 'text or vector similarity' which aligns with the schema's oneOf structure for the query parameter. However, with 0% schema description coverage, the description doesn't add meaningful details about parameter usage, format requirements, or search behavior. It provides basic context but doesn't compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as searching for entities and their relations using text or vector similarity. It specifies both the resource (entities and relations) and the method (text or vector similarity search). However, it doesn't explicitly differentiate from sibling tools like 'read_graph' which might also involve reading/searching operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this search tool should be used instead of 'read_graph' or other siblings, nor does it provide any context about prerequisites, limitations, or appropriate scenarios for text versus vector search.
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.
6 tool updates
v1.0.0- First observed
create_entities - First observed
create_relations - First observed
delete_entity - First observed
delete_relation - First observed
read_graph - First observed
search_nodes
TDQS
Scored across 6 tools
Every tool has a clearly distinct purpose: create_entities and create_relations handle creation of different data types, delete_entity and delete_relation handle deletion of different data types, read_graph retrieves recent data, and search_nodes performs searches. There is no overlap or ambiguity between these functions.
All tools follow a consistent verb_noun pattern using snake_case: create_entities, create_relations, delete_entity, delete_relation, read_graph, and search_nodes. The naming is predictable and uniform throughout the set.
With 6 tools, this server is well-scoped for a memory/graph database system. Each tool serves a clear and necessary function (create, delete, read, search) without being too sparse or bloated, fitting typical expectations for such a domain.
The toolset covers core CRUD operations for entities and relations (create, delete, read, search), but lacks explicit update tools for modifying existing entities or relations. Agents can work around this by deleting and recreating, but it's a minor gap in lifecycle coverage.
Maintenance
Related MCP Connectors
Cloud-hosted MCP server for durable AI memory
Capability registry for the agentic economy. Semantic search over verified MCP server listings.
Shared cross-LLM long-term memory over MCP: semantic recall, sessions, and media (pgvector).
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- FlicenseNot gradedqualityDmaintenanceAn MCP server for managing persistent AI memory using hybrid search (keyword + semantic vector) with SQLite storage and offline-first local embeddings.-
- AlicenseAqualityDmaintenanceA high-performance MCP server providing long-term memory storage with semantic and keyword search capabilities, using SQLite and fastembed for sub-200ms queries.5GPL 3.0