
Dakera Mcp
io.github.Dakera-AI由 GitHub 帳號 Dakera-AI 發布v0.12.3更新於 Oct 27, 2026
Agent memory engine — 106 MCP tools, self-hosted, single Rust binary
概覽
為 AI 助理提供持久、可檢索記憶的 MCP 伺服器,後端為自架的 Dakera 執行個體,預設載入 14 個工具,可依需求擴充。
- 可以做什麼
- 以語意查詢、標籤、類型或向量加全文混合方式儲存、召回與搜尋記憶
- 依工作階段與代理組織記憶,並依標籤、類型或時間範圍批次召回或批次刪除
- 從種子記憶建立知識圖譜,並從自由文字中擷取實體與結構
- 將文件匯入為帶引用的分塊記憶,並在伺服器開啟相關功能時管理附件、語音轉錄與影像索引
- 依需求探索並載入更多工具定義,查詢伺服器健康狀態、能力、加密與遷移狀態
- 適用情境
- 適合需要助理跨工作階段記住資訊,並對自身儲存的筆記、文件或對話歷史做語意檢索的情境。也適合已運行或打算自架 Dakera 記憶伺服器、希望預設工具集精簡、需要時再擴充的團隊。
- 執行需求
- 需要一個可透過 HTTP 存取的 Dakera 記憶伺服器(v0.11.108 或 v0.12.0 至 v0.12.3)以及 Dakera API 金鑰。本機程序可透過 npm 套件、Homebrew、Cargo、Docker 映像或預先編譯的執行檔安裝。設定使用 DAKERA_API_URL 與 DAKERA_API_KEY 環境變數,DAKERA_MCP_PROFILE 用來選擇工具設定檔。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Dakera Mcp,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
⚡ dakera-mcp
[CI] [Crate] [npm] [Downloads] [License: MIT] [LoCoMo 88.2%] [Glama] [Docs] [dakera.ai] [Playground]
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
Architecture: 14 core tools + on-demand discovery
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:
- 14 tools loaded by default — the 12 highest-frequency memory operations + 2 meta-discovery tools
- On-demand expansion — use
dakera_discover_toolsanddakera_load_toolsto fetch additional tool schemas only when you need them
Default tool set (core profile)
Profiles & token cost
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).
Accessing additional tools
Profile selection
The profile controls which tools appear in tools/list. Three ways to set it:
1. Per-request (in tools/list params):
2. Environment variable (applies to all requests):
3. Default: core (14 tools, ~3,400 tokens)
Dakera v0.12
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.
New with Dakera v0.12.2
What is new
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:<hex> of an attachment in the agent's own namespace,
_dakera_agent_<agent_id>). 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.
Recall: top_k ranked results, context apart
dakera_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.
Documents (Dakera v0.12.3)
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):
A recall result that is a chunk carries citation: document_id, title, page_start /
page_end, heading_path, chunk_index / chunk_count (from memory.metadata._dakera_document).
On a server before v0.12.3 (no documents section in /v1/capabilities) the tools are not listed and
a direct call says they need v0.12.3; DAKERA_DOCUMENTS=0 on the server hides them the same way.
Features the server has off are left out
The opt-in features (attachments, speech to text, image indexing) are off by default on the server.
dakera-mcp asks GET /v1/capabilities (once a minute, 3 s timeout) before it lists tools:
attachmentsoff, or a server without/v1/capabilities(v0.11): thedakera_attachment_*tools are not listed and not returned bydakera_discover_tools; a direct call answers with the variable to set (DAKERA_ATTACHMENTS=1) and makes no request.visionoff:dakera_attachment_index_imageis left out (DAKERA_VISION=1turns it on).- A server without
/v1/capabilities(v0.11) also hidesdakera_encryption_statusanddakera_embed_migration_status, whose routes are new in v0.12. - The server cannot be asked (down, starting, key refused): nothing is hidden (asked again after 10 s).
The default core profile has no opt-in tools, so it never makes that request.
Errors
Error answers keep the server's text and add a Hint: line for the v0.12 cases: a key pinned to
namespaces gets 403 on node-wide /admin routes (backups, encryption, quotas, config); backup
download, upload and restore need super_admin; 413 (body over a limit, or a hard quota), 501
(feature off), 503 (Retry-After, which the retry logic now honours, up to 8 s) and 429 (rate
limit). A route the server lacks (an older server) and a v0.11 rotation without new_key get a hint
too. Since Dakera v0.12.2 a refused argument gets 400 with a message naming the field
(content: content exceeds maximum of 100000 bytes …, tags[1]: … reserved …); the message is
passed on and the hint names the field to correct (the content limit counts UTF-8 bytes). The 403
hints point at dakera_whoami. From Dakera v0.12.3 the admin key, backup and rotate-key
routes refuse a field they do not know (422); the hint names it. A request that timed out is retried only when it is safe to repeat (GET, PUT, DELETE): a store,
an import or a key rotation is never sent twice.
Run Dakera
The MCP server connects to a Dakera memory server. You need one running first:
For persistent storage (recommended):
Full deployment guide (Docker Compose, Kubernetes, Helm): dakera-deploy
Install
npm / npx (Node.js 18+)
Homebrew (macOS / Linux)
Cargo
Docker
The image is distroless (no shell, no package manager) and runs as the non-root user 65532.
Binary download
Pre-built binaries for macOS, Linux, and Windows are available on the releases page.
Connect
Add to .mcp.json (Claude Code) or claude_desktop_config.json (Claude Desktop):
dakera_extract_entities calls the ODE sidecar at DAKERA_ODE_URL; those requests currently carry
the Dakera API key as well (tracked in Dakera-AI/dakera#924).
To start with the power profile (exposes up to 86 tools):
Why This Exists
AI agents forget everything when the session ends. Dakera fixes that. This MCP server gives your agent a persistent memory layer with zero infrastructure overhead — point it at a Dakera instance and it works.
The 14-tool default keeps your context window lean. The meta-tools let you expand on demand when you need advanced operations like bulk vector upsert, knowledge graph traversal, or memory federation.
→ dakera.ai for hosted instance
→ Self-host with dakera-deploy
Documentation
Related
dakera.ai · Documentation · Request Early Access
Part of the Dakera AI open-core ecosystem. Built with Rust. Self-hosted. Zero dependencies.
來源:README.md,提交 70c9cc2
工具
0版本歷史
5- v0.12.3最新Oct 22, 2026
- v0.12.2Oct 8, 2026
- v0.12.0Oct 5, 2026
- v0.11.0Oct 1, 2026
- v0.10.12Sep 16, 2026
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