lore
lore
Claude Code 및 OpenAI Codex CLI 대화 전반에 걸친 의미론적 검색을 제공합니다. 모든 프로젝트, 모든 세션, 모든 브랜치, 모든 에이전트에 걸쳐 이전에 논의했던 모든 내용을 찾아보세요.
기능
하이브리드 검색 (벡터 + 키워드) multilingual-e5-small 임베딩과 FTS5/BM25를 Reciprocal Rank Fusion을 통해 결합합니다. 의미와 정확한 용어 모두를 기반으로 결과를 찾습니다.
멀티 에이전트: Claude Code + Codex CLI
~/.claude/projects/(Claude Code)와~/.codex/sessions/(OpenAI Codex CLI)를 동일한 DB에 인덱싱합니다. Codex 세션은session_meta의cwd별로 그룹화되어codex-<path>가상 프로젝트로 표시되므로, 함께 검색하거나 특정 에이전트로 필터링할 수 있습니다.완전 로컬, API 키 불필요 모든 작업이 사용자 기기에서 실행됩니다. 임베딩을 위한 ONNX Runtime, 저장을 위한 sqlite-vec을 사용합니다. 기기 밖으로 데이터가 유출되지 않습니다.
세션 종료 시 자동 인덱싱 SessionEnd 훅이 백그라운드에서 모든 새 세션을 자동으로 인덱싱합니다. 수동 트리거가 필요 없습니다.
백그라운드 인덱싱 수동 인덱스 트리거는 즉시 반환됩니다. 작업하는 동안 진행 상황을 모니터링하세요. 인덱싱이 완료되는 동안 이미 인덱싱된 내용을 검색할 수 있습니다.
기본 옵트아웃(Opt-out) 모든 프로젝트가 자동으로 인덱싱됩니다. 원하지 않는 프로젝트는 제외할 수 있습니다. 등록이 필요 없습니다.
대화 인식 청킹(Chunking) 임의의 토큰 윈도우가 아닌 논리적 턴(사용자 질문 + 전체 어시스턴트 응답 체인) 단위로 분할합니다. 도구 사용 체인, 사고 블록, 다단계 상호작용을 올바르게 처리합니다.
100개 이상의 언어 지원 한국어, 일본어, 중국어, 영어 등 90개 이상의 언어를 지원합니다. 정확한 청킹을 위해 CJK 인식 토큰 추정 기능을 제공합니다.
Related MCP server: Semantic Search MCP Server
빠른 시작
Claude Code에 추가
# No install needed — always runs latest version
claude mcp add -s user lore -- npx getlore
# Or for a single project only
claude mcp add -s project lore -- npx getloreOpenAI Codex CLI에 추가
# No install needed
codex mcp add lore -- npx getlorenpm install -g getlore
# Then register with your tool:
claude mcp add -s user lore -- getlore # Claude Code
codex mcp add lore -- getlore # Codex CLI
# Manage your install:
getlore --version # Check installed version
getlore update # Update to latest사용법
연결되면 AI가 lore의 도구를 직접 사용할 수 있습니다:
You: "What did we discuss about auth refactoring last week?"
Claude: [calls lore search] Found 3 relevant conversations...
In your "my-webapp" project on March 15, you decided to...최초 설정:
인덱싱(Index) --
index()는 모든 프로젝트를 자동으로 스캔하며 백그라운드에서 실행됩니다.검색(Search) -- 과거 대화에 대해 무엇이든 물어보세요.
제외(Exclude) (선택 사항) -- 관심 없는 노이즈 프로젝트를 숨깁니다.
도구
도구 | 목적 |
| 인덱싱에서 프로젝트 제외/포함 (옵트아웃 모델) |
| 백그라운드 인덱싱 시작. 제외되지 않은 모든 프로젝트 대상. 모드: |
| 인덱싱 진행 상황, 예상 완료 시간, 건너뛴 이유, DB 상태 확인 |
| 대화 전반에 걸친 의미론적 + 키워드 검색 |
| 주변 대화 내용을 포함하여 검색 결과 확장 |
| 프로젝트별로 인덱싱된 세션 탐색 |
이 도구가 필요한 이유
Claude Code는 모든 대화를 ~/.claude/projects/에 JSONL 기록으로 저장하며, OpenAI Codex CLI는 롤아웃을 ~/.codex/sessions/YYYY/MM/DD/에 저장합니다. 몇 주가 지나면 수십 개의 프로젝트에 걸쳐 수백 개의 세션이 쌓이게 되며, 아키텍처 결정, 디버깅 세션, 코드 리뷰, 디자인 탐색 등 두 에이전트 모두에 분산되어 저장됩니다.
하지만 이를 검색할 방법이 없습니다. "인증 미들웨어에 대해 어떤 접근 방식을 취했지?" 또는 "데이터베이스 마이그레이션 논의가 있었던 프로젝트가 어디지?"와 같은 질문을 할 수 없습니다.
기존 도구들은 클라우드 API가 필요하거나, 좀비 프로세스를 생성하거나, 대화를 일반 문서로 취급합니다. lore는 AI 코딩 세션을 위해 특별히 제작되었습니다. 턴 경계, 도구 사용 체인, 사고 블록을 이해하며 Claude Code와 Codex JSONL 형식을 모두 기본적으로 파싱합니다. Node.js 외에 다른 의존성 없이 완전히 로컬에서 실행됩니다.
작동 원리
~/.claude/projects/*/*.jsonl ~/.codex/sessions/YYYY/MM/DD/rollout-*.jsonl
\ /
\ /
JSONL Parser (Claude Code + Codex formats, skips noise)
|
Turn-pair Chunker (groups by logical conversation turns)
|
Transformers.js (multilingual-e5-small, INT8 quantized, 384d)
|
sqlite-vec + FTS5 (hybrid vector + keyword storage)
|
Reciprocal Rank Fusion (combines both signals for ranking)Codex 세션은 각 파일의 session_meta 라인에서 추출된 cwd별로 그룹화되어 인덱스 내에 codex-<path> 가상 프로젝트로 표시됩니다.
저장소: ~/.lore/lore.db에 단일 SQLite 파일로 저장되며, 동시 읽기를 위해 WAL 모드를 사용합니다.
설정: 프로젝트 제외 설정은 ~/.lore/config.json에 저장됩니다.
환경 변수
변수 | 기본값 | 설명 |
|
| 데이터 디렉토리 |
|
| 데이터베이스 경로 |
|
| Claude Code 기록 위치 |
|
| OpenAI Codex CLI 롤아웃 위치 |
Apple Silicon (M 시리즈) 기준 측정:
지표 | 값 |
검색 지연 시간 | 20-30ms |
인덱싱 속도 | 초당 약 10개 세션 |
첫 검색 (콜드 모델 로드) | 약 5초 |
DB 크기 | 10개 세션당 약 0.1MB |
모델 크기 (1회 다운로드) | 약 112MB |
"세션을 찾을 수 없음"
manage_projects를 list 액션과 함께 실행하여 사용 가능한 프로젝트를 확인하세요. 제외하지 않는 한 모든 프로젝트가 기본적으로 인덱싱됩니다.
오래된 잠금 파일
인덱싱이 중단된 경우, 다음 실행 시 잠금 파일이 자동으로 정리됩니다 (PID 기반 감지).
DB 손상
~/.lore/lore.db를 삭제하고 다시 인덱싱하세요. 원본 데이터(~/.claude/projects/)는 절대 수정되지 않습니다.
개발
git clone https://github.com/hyunjae-labs/lore.git
cd lore
npm install
npm run build
npm test # 135 tests기술 스택
Model Context Protocol SDK -- stdio 전송
@huggingface/transformers -- multilingual-e5-small (INT8)
better-sqlite3 + sqlite-vec -- 임베디드 벡터 DB
Reciprocal Rank Fusion -- 하이브리드 검색 랭킹
라이선스
MIT
Available Tools
6 toolsget_contextA
Retrieve more conversation context around a specific search result. Use ONLY after calling search, when you need to see what was discussed before or after a result.
| Name | Required | Description | Default |
|---|---|---|---|
| chunk_id | Yes | ||
| direction | No | ||
| count | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool retrieves context, it lacks details on permissions, rate limits, error handling, or what the output looks like (e.g., format, size limits). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, with two sentences that directly state the purpose and usage guidelines without any wasted words. Every sentence earns its place by providing essential information efficiently.
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 moderate complexity (3 parameters, no output schema, no annotations), the description covers purpose and usage well but is incomplete. It lacks details on parameters, behavioral traits, and output format, which are necessary for full understanding. The description is adequate as a minimum but has clear gaps in 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 0%, so the description must compensate for undocumented parameters. It only vaguely references 'a specific search result' (implied to relate to 'chunk_id') and 'before or after a result' (implied to relate to 'direction'), but provides no specifics on parameter meanings, formats, or constraints. This fails to adequately explain the three parameters beyond basic schema hints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve more conversation context') and resource ('around a specific search result'), distinguishing it from siblings like 'search' (which finds results) or 'list_sessions' (which lists sessions). It explicitly defines the tool's scope as fetching contextual conversation snippets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('Use ONLY after calling search, when you need to see what was discussed before or after a result'), including a prerequisite (must call 'search' first) and a clear use-case (viewing surrounding context). It effectively differentiates from alternatives by specifying its post-search role.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
indexA
Update the search index with recent Claude Code sessions. Call if search returns stale results or the user asks to refresh the index. Modes: 'incremental' (default, only new/changed), 'full' (delete all and rebuild from scratch), 'cancel' (stop running index).
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | ||
| project | No | ||
| confirm | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining the three modes and their behaviors ('incremental' for new/changed, 'full' for delete and rebuild, 'cancel' to stop). It could mention performance impact or permissions but covers core operational traits.
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?
Three sentences with zero waste: first states purpose, second gives usage guidelines, third details modes. Each sentence earns its place, and the structure is front-loaded with essential 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?
For a tool with 3 parameters, no annotations, and no output schema, the description is quite complete—covering purpose, usage, and key parameter semantics. It could note that 'full' mode might be resource-intensive or that 'confirm' is for safety, but it's largely adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the 'mode' parameter's three values and their meanings, which adds crucial semantics beyond the bare enum in the schema. It doesn't cover 'project' or 'confirm', but the mode explanation is substantial.
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 ('Update the search index') and resources ('recent Claude Code sessions'), distinguishing it from sibling tools like 'search' or 'list_sessions' which query rather than update the index.
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?
Explicit guidance is provided on when to use this tool: 'if search returns stale results or the user asks to refresh the index.' This directly addresses the tool's purpose relative to alternatives like 'search'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sessionsB
List all indexed Claude Code sessions. Use when the user wants to browse conversation history or find sessions by project/date.
| Name | Required | Description | Default |
|---|---|---|---|
| project | No | ||
| limit | No | ||
| sort | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that sessions are 'indexed' and implies filtering capabilities ('by project/date'), but lacks details on permissions, rate limits, pagination, or what 'indexed' entails. For a list tool with zero annotation coverage, this leaves significant gaps in understanding the tool's behavior and constraints.
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 concise and well-structured, consisting of two sentences that efficiently convey the tool's purpose and usage. The first sentence states what it does, and the second provides context for when to use it, with no wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects like permissions or rate limits, and parameter semantics are underspecified. Without an output schema, it also doesn't describe return values (e.g., session format). For a tool with moderate complexity and no structured support, the description should provide more comprehensive guidance.
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 3 parameters with 0% description coverage, meaning no parameter details are documented in the schema. The description only vaguely references 'project/date' for filtering, which partially covers the 'project' parameter but ignores 'limit' and 'sort'. It doesn't explain what 'limit' controls (e.g., number of results) or the meaning of 'sort' enum values ('recent', 'oldest'), failing to compensate for 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 clearly states the tool's purpose: 'List all indexed Claude Code sessions.' It specifies the verb ('List') and resource ('indexed Claude Code sessions'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this tool from sibling tools like 'search' or 'get_context', which might also involve session retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage guidance: 'Use when the user wants to browse conversation history or find sessions by project/date.' This gives context for when to invoke the tool, such as for browsing or filtering by project/date. It doesn't explicitly state when not to use it or name alternatives like 'search', but the context is sufficient for basic decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
manage_projectsA
Manage which projects are registered for indexing. Use 'list' to see all projects on disk and their registration status. Use 'add' to register a project for indexing. Use 'remove' to unregister. Projects must be registered before they can be indexed.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| project | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the three discrete actions and the registration requirement, but doesn't mention permissions needed, whether changes are reversible, rate limits, or what the response looks like. For a mutation tool with zero annotation coverage, this leaves significant 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 efficiently structured with three sentences: an overview statement, specific action explanations, and a prerequisite. Every sentence adds value with no redundant information, making it easy to parse and 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?
For a 2-parameter tool with no annotations and no output schema, the description provides good purpose and usage guidance but lacks details about response format, error conditions, and the exact format of the 'project' parameter. It's adequate but has clear gaps in behavioral transparency.
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?
With 0% schema description coverage, the description must compensate. It explains the meaning of the 'action' parameter values ('list', 'add', 'remove') and implies the 'project' parameter is used with 'add' and 'remove' actions. However, it doesn't specify what format the 'project' parameter expects (path, name, ID).
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 ('manage', 'list', 'add', 'remove') and resources ('projects', 'indexing'), distinguishing it from sibling tools like 'index' or 'search'. It explains that this tool handles registration status for indexing, not the indexing process itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use each action ('list' to see status, 'add' to register, 'remove' to unregister) and includes a prerequisite statement ('Projects must be registered before they can be indexed') that helps differentiate from the 'index' sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchB
Search through past Claude Code conversations across all projects. Use when the user asks about previous discussions, past decisions, or anything from a prior conversation.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| project | No | ||
| branch | No | ||
| after | No | ||
| before | No | ||
| limit | No |
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 scope (across all projects), it doesn't describe what the search returns (snippets, full conversations, metadata), whether there are rate limits, authentication requirements, or how results are ordered/paginated. For a search tool with 6 parameters, 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 perfectly concise with two sentences that each earn their place. The first states what the tool does, and the second provides usage guidance. There's zero wasted text or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (search tool with 6 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns, how results are structured, or provide any parameter guidance. For a tool that presumably returns search results, the lack of output information is a significant 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?
Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions none of the 6 parameters, not even the required 'query' parameter. While the usage context implies a search query, it doesn't explain what format the query should take, what the project/branch parameters filter, or what the date/time parameters expect.
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 through past Claude Code conversations across all projects, which is a specific verb+resource combination. However, it doesn't explicitly distinguish this search tool from potential sibling tools like 'get_context' or 'list_sessions' that might also retrieve conversation data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'when the user asks about previous discussions, past decisions, or anything from a prior conversation.' This gives good guidance but doesn't explicitly state when NOT to use it or mention alternatives among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
statusA
Check the health and progress of lore indexing. Shows indexing status, session counts, DB size. Use this to monitor indexing progress after calling index.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (checking health/progress and showing specific metrics) but lacks details on permissions needed, rate limits, or what happens if indexing isn't running. It doesn't contradict annotations, but could be more informative.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded: the first states the purpose and what it shows, the second provides usage guidance. Every sentence adds value without redundancy or waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is reasonably complete. It explains the tool's purpose, what it returns, and when to use it. However, without an output schema, it could benefit from more detail on return format or error conditions, but this is minor for a status-check tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter information, but it implicitly confirms no parameters are needed by not mentioning any. This meets the baseline for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('check', 'shows') and resources ('health and progress of lore indexing', 'indexing status, session counts, DB size'). It distinguishes from siblings by focusing on monitoring rather than performing operations like 'index' or '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 clear context for usage ('Use this to monitor indexing progress after calling index'), indicating when to use it in relation to the 'index' sibling tool. However, it doesn't explicitly state when not to use it or mention alternatives among other siblings like 'list_sessions' or 'get_context'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
6 tool updates
- First observed
get_context - First observed
index - First observed
list_sessions - First observed
manage_projects - First observed
search - First observed
status
TDQS
Each tool has a clearly distinct purpose with no overlap: get_context retrieves conversation context around search results, index updates the search index, list_sessions lists indexed sessions, manage_projects handles project registration, search performs searches, and status checks indexing health. The descriptions explicitly differentiate their use cases, preventing agent confusion.
Tool names follow a consistent snake_case pattern and use clear verbs like get, index, list, manage, search, and status. However, 'status' deviates slightly as a noun rather than a verb (e.g., 'check_status' would be more consistent), but overall the naming is predictable and readable.
With 6 tools, this server is well-scoped for its purpose of managing and searching conversation history. Each tool serves a specific function in the indexing and retrieval workflow, from setup (manage_projects, index) to query (search, get_context) and monitoring (list_sessions, status), with no unnecessary bloat.
The tool set provides complete coverage for the domain of indexing and searching Claude Code sessions. It includes project management (manage_projects), indexing operations (index, status), session listing (list_sessions), search functionality (search), and context retrieval (get_context), ensuring agents can handle the full lifecycle without gaps.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Persistent memory for Claude Code and Cursor. Stop re-explaining your project every session.
Search your knowledge bases from any AI assistant using hybrid RAG.
Code intelligence for coding agents: semantic, AST, graph, and full-text search. 279+ languages.
Project memory, semantic code search, and grounded agent context.
Related MCP Servers
- -licenseNot gradedqualityNot gradedmaintenanceEnables comprehensive search and analysis of Claude Code conversation history using full-text search, optional semantic vector search, and conversation management tools. Provides fast SQLite-based indexing with role-based filtering, project organization, and hybrid search capabilities combining keyword and semantic matching.-
- FlicenseNot gradedqualityBmaintenanceProvides hybrid semantic and keyword code search for Claude Code using BM25 and vector retrieval. It enables indexing and searching local codebases with language-aware chunking and local embeddings.-
- AlicenseAqualityAmaintenanceEnables local semantic search over documents and code for Claude Code and Claude Desktop, running entirely offline with local embeddings and vector storage.123MIT
- AlicenseNot gradedqualityCmaintenanceProvides Claude Code with local semantic search and indexing of your codebase using AST-aware chunking and hybrid search, enabling deep code understanding without sending data to the cloud.MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/hyunjae-labs/lore'
If you have feedback or need assistance with the MCP directory API, please join our Discord server