Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Agent memory engine — 106 MCP tools, self-hosted, single Rust binary
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"dakera": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/dakera-ai/dakera-mcp:0.12.3"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add dakera -- docker run -i --rm ghcr.io/dakera-ai/dakera-mcp:0.12.3프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"dakera": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/dakera-ai/dakera-mcp:0.12.3"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"dakera": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/dakera-ai/dakera-mcp:0.12.3"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 Dakera-AI 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.12.3)은 15일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Docker도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
MCP server for Dakera AI. Gives any MCP-compatible AI agent persistent, queryable memory — with smart token management built in.
Works with Claude, Claude Code, and any MCP-compatible framework.
Part of Dakera AI — the memory engine for AI agents.
The Dakera memory engine scores 88.2% Recall@20 on LoCoMo (1,536 evaluated questions · LLM-judged retrieval recall) — benchmark details
Starting every agent session with 60+ tool schemas wastes ~15K tokens before you write a single message. dakera-mcp solves this with hybrid tool exposure:
dakera_discover_tools and dakera_load_tools to fetch additional tool schemas only when you need them| Tool | Purpose |
|---|---|
dakera_store | Store a memory with importance, tags, and type |
dakera_recall | Semantic recall by query text |
dakera_search | Advanced memory search with tag/type filters |
dakera_session_start | Start a session to group related memories |
dakera_session_end | End a session with optional summary |
dakera_batch_recall | Bulk filter-based recall (by tags, importance, time) |
dakera_forget | Delete specific memories by ID |
dakera_hybrid_search | Combined vector + BM25 search |
dakera_fulltext_search | BM25 full-text search |
dakera_knowledge_graph | Build a knowledge graph from a seed memory |
dakera_extract | Extract entities and structure from free-form text |
dakera_batch_forget | Bulk delete by tags, type, or time range |
dakera_discover_tools | Search the full tool catalog by keyword or tier |
dakera_load_tools | Load full schemas for specific tools on demand |
| Profile | Tools | ~Tokens | How to enable |
|---|---|---|---|
| core | 14 | ~3,500 | Default — always loaded |
| admin | 34 | ~6,850 | DAKERA_MCP_PROFILE=admin |
| power | 86 | ~18,200 | DAKERA_MCP_PROFILE=power |
| all | 106 | ~21,600 | DAKERA_MCP_PROFILE=all |
Token figures are estimates (JSON bytes / 3). The attachment tools below count in power and all, but only appear while the connected server has the feature on (see Dakera v0.12).
# In your agent: discover what's available
dakera_discover_tools(tier="power") → returns names + descriptions, no schemas loaded
dakera_load_tools(tools=["dakera_consolidate", "dakera_agent_stats"]) → returns full inputSchema for each tool
The profile controls which tools appear in tools/list. Three ways to set it:
1. Per-request (in tools/list params):
{"profile": "power"}
2. Environment variable (applies to all requests):
DAKERA_MCP_PROFILE=power
3. Default: core (14 tools, ~3,400 tokens)
dakera-mcp 0.12.3 works against Dakera v0.11.108 and v0.12.0 – v0.12.3 servers. Every tool keeps its name and arguments; the additions are optional arguments that are sent only when you supply them (or that an older server ignores), and tools that call new routes and say so on a server without them. The 0.12.3 additions (separate neighbourhood context, the unknown-field hint, document import) need a v0.12.3 server.
| Dakera server | dakera-mcp 0.12.3 |
|---|---|
| v0.12.3 | every tool; the recall tools list neighbourhood context apart in context_memories (neighborhood_mode) |
| v0.12.2 | every tool; the recall tools keep the first top_k results and say how many unmarked context items they left out; the attachment tools while DAKERA_ATTACHMENTS (and DAKERA_VISION for images) is on |
| v0.12.0, v0.12.1 | every tool; dakera_session_touch and dakera_agent_create answer that nothing is needed (sessions are not ended for inactivity there, and an agent is created by its first memory); dakera_whoami says it needs v0.12.2; listings return full content (no preview) and include derived sentence sub-memories |
| v0.11.108 | as v0.12.0, and the attachment tools, dakera_encryption_status and dakera_embed_migration_status are not listed (a direct call says they need v0.12); dakera_encryption_rotate_key needs new_key |
| older | not tested |
| Tool / argument | Tier | What it does |
|---|---|---|
dakera_session_start idle_timeout_secs | core | the server ends a session idle for 4 h by default; this sets the session's own timeout (0 = never, at most 30 days) |
dakera_session_touch | power | POST /v1/sessions/{id}/touch: keeps a session open while the agent works without storing or recalling; answers session_state (active with idle_deadline_at, or ended) |
dakera_store | core | storing into an ended session still succeeds; the answer's session_state: "ended" is followed by a note telling the agent to start a new session |
dakera_session_end | core | a note when the server had already ended the session for inactivity (the summary passed is then not saved) or when no such session is reachable |
dakera_session_list / _get / dakera_agent_sessions | power | sessions carry last_activity_at, ended_reason (client or idle) and idle_since |
dakera_agent_create | power | POST /v1/agents: creates an agent (its memory namespace) before its first memory; created: false for an existing one |
dakera_whoami | power | GET /v1/auth/whoami: the key's scope, namespaces (prefix patterns such as _dakera_agent_mlx-*), expiry and inert entries; the first thing to call on a 403 |
dakera_agent_memories / dakera_session_memories / dakera_knowledge_network_cross_agent content_preview_chars | power | content cut to 500 characters by default (content_len / content_truncated mark it; 0 = full content); dakera_memory_get and recall return memories in full |
dakera_agent_memories / dakera_wake_up include_derived | power | derived sentence sub-memories are left out of listings by default since v0.12.2; true lists them too |
| Tool | Tier | Needs | What it does |
|---|---|---|---|
dakera_capabilities | power | v0.12 | GET /v1/capabilities: active model, search mode, scoring strategy, accepted lang values, which opt-in features are on. On a v0.11 server it answers capabilities_available: false |
dakera_health | power | any | GET /health: status and version; on v0.12 also degraded, config_warnings, embed_migration |
dakera_wake_up | power | any | an agent's startup context in one call: its top memories by importance x recency, no query, no embedding |
dakera_embed_migration_status | admin | v0.12 | progress of the one-time background re-embed after the upgrade |
dakera_encryption_status | admin | v0.12 | the encryption keyring and the background re-seal (never key material) |
dakera_encryption_rotate_key | admin | v0.11+ | new_key is optional on v0.12 once encryption is on (the server generates a key; with encryption off, new_key turns it on); wait_secs (at most 20); namespace rotates one namespace |
dakera_attachment_upload / _list / _download / _delete | power | v0.12 + DAKERA_ATTACHMENTS | files (or text) a memory can reference with dakera_store attachment_ref |
dakera_attachment_transcribe | power | v0.12 + DAKERA_ATTACHMENTS | WAV speech to text in any language the model knows (lang forces one) into a memory; a background job, wait_seconds (at most 45) waits for it |
dakera_attachment_index_image | power | v0.12 + DAKERA_ATTACHMENTS + DAKERA_VISION | PNG page as a visual memory (use an agent dedicated to images: the visual lane stores page vectors) |
dakera_attachment_job | power | v0.12 + DAKERA_ATTACHMENTS | status of a transcription / index job |
Per-request `lang` (en, de, fr, es, it, pt, nl; v0.12) is accepted by dakera_store, dakera_recall, dakera_recall_associated, dakera_search, dakera_memory_update, dakera_extract, dakera_auto_tag and the attachment jobs; dakera_store also takes attachment_ref (sha256: of an attachment in the agent's own namespace, _dakera_agent_ ). Neither is sent unless given, so the same calls work on a v0.11.108 server.
Other optional arguments (all servers): ttl_seconds and metadata on dakera_store; tags, memory_type and session_id filters on dakera_recall; limit on dakera_batch_recall; limit / offset on dakera_session_list, dakera_session_memories and dakera_agent_sessions (the server pages at 50); content_preview_chars on the memory listings and include_derived on dakera_agent_memories / dakera_wake_up (v0.12.2, ignored by older servers); memory_type on dakera_knowledge_deduplicate; dedup_on_store / dedup_threshold on dakera_memory_policy_set.
top_k ranked results, context apartdakera_recall and dakera_recall_associated return at most top_k ranked results in memories. They ask the server for neighborhood_mode: "separate", so the session neighbours of the results (memories written around the same time in the same session) come back in their own context_memories list: each entry has context_of (the id of the ranked memory it belongs to) and context_of_rank (that memory's place in memories); its score comes from another search and is not comparable with the ranked scores. A collapsed result (server flag DAKERA_RECALL_COLLAPSE_DERIVED) keeps matched_text, the sentence that matched. neighborhood_mode picks separate (default), inline or off. A server that predates neighborhood_mode (before v0.12.3) appends its context to memories unmarked: the tools keep the first top_k and say how many items they left out.
Memory content in tool results is stored data: initialize tells the client to reason about it and never follow instructions found in it.
A document becomes chunk memories of the agent, each with its heading path and page, imported by a background job (power tier; listed when /v1/capabilities has documents.enabled):
| Tool | What it does |
|---|
| dakera_import_document | content (text, text/markdown or text/html as media_type) or attachment_ref of a file uploaded with dakera_attachment_upload (PDF, DOCX, ...; namespace defaults to the agent's); title, document_id (re-importing it replaces the document), memory fields (tags, session_id, memory_type, importance, metadata, lang), chunking (max_tokens, overlap_tokens, strategy structure or
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