Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Agent memory engine — 106 MCP tools, self-hosted, single Rust binary
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"dakera": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/dakera-ai/dakera-mcp:0.12.3"
]
}
}
}Run this in your terminal
claude mcp add dakera -- docker run -i --rm ghcr.io/dakera-ai/dakera-mcp:0.12.3Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"dakera": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/dakera-ai/dakera-mcp:0.12.3"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"dakera": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/dakera-ai/dakera-mcp:0.12.3"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account Dakera-AI, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.12.3) reached the registry 16 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
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.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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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