MIND MCP Server — The most complete AI memory layer available. 15 tools, 89 actions.
Your AI agents forget everything between sessions. MIND fixes that. Connect any MCP-compatible agent to your personal knowledge graph — with emotional intelligence, CRM, life management, social features, self-training, autonomous insights, and more.
| Tool |
Actions |
What It Does |
mind_query |
1 |
Semantic search across your knowledge graph (5 search modes) |
mind_remember |
5 |
Store, search, get, list, delete — documents, entries, thoughts |
mind_context |
1 |
Load persistent identity, preferences, rules, priorities, recent activity |
mind_life |
12 |
Goals, projects, tasks + full calendar management + productivity stats |
mind_crm |
7 |
Contacts, pipeline, activity logging, interaction history |
mind_graph |
3 |
Graph stats, diagnostics, entity labels |
mind_admin |
7 |
User provisioning, featured minds, tier/credit management |
mind_sense |
7 |
MINDsense emotional intelligence — state, signals, timeline, KG weights, spikes |
mind_research |
3 |
Launch autonomous deep research jobs |
mind_train |
7 |
Self-training sessions + save chats to knowledge graph |
mind_social |
14 |
Thoughts (posts), social feed, communities, likes, comments |
mind_profile |
9 |
Profile, custom system prompts, LLM model selection |
mind_insights |
7 |
Autonomous Learning Engine insights, weekly summaries, feedback |
mind_automate |
6 |
Scheduled automations, event triggers, execution history |
mind_notify |
4 |
Notifications, mark read, stats |
Sign up at m-i-n-d.ai → Settings → Developer → Create API Key
npm install -g @astramindapp/mcp-server
claude mcp add mind -- env MIND_API_KEY=mind_xxx mind-mcp
Add to your claude_desktop_config.json:
{
"mcpServers": {
"mind": {
"command": "mind-mcp",
"env": {
"MIND_API_KEY": "mind_your_key_here"
}
}
}
}
Add to .cursor/mcp.json:
{
"mcpServers": {
"mind": {
"command": "mind-mcp",
"env": {
"MIND_API_KEY": "mind_your_key_here"
}
}
}
}
The server works with any tool that supports Model Context Protocol — Claude, GPT, Gemini, Llama, or any future model.
Your AI Agent <--> MCP Protocol <--> MIND MCP Server <--> Personal Knowledge Graph
|
Emotional Intelligence
Autonomous Learning
CRM + Life + Social
- Agent calls
mind_context at session start → loads identity, rules, priorities
- Agent calls
mind_query before decisions → retrieves relevant memories
- Agent calls
mind_remember after tasks → stores outcomes and learnings
- Agent calls
mind_sense → reads user's emotional state to adapt responses
- Agent calls
mind_insights → surfaces autonomous pattern detection
- Next session, any agent has full context. Knowledge compounds.
|
Flat Files |
MIND |
| Size |
~20K chars, truncated |
Unlimited knowledge graph |
| Retrieval |
Loads everything every turn |
Only relevant memories via semantic search |
| Structure |
Unstructured text |
Graph with entities, relationships, and emotional weights |
| Cross-agent |
One tool only |
Shared across all AI agents |
| Intelligence |
None |
Autonomous pattern detection + emotional encoding |
|
Anthropic Official |
Mem0 |
Graphiti/Zep |
MIND |
| Tools |
8 |
4 |
9 |
15 |
| Knowledge graph |
Basic (JSON) |
No (vectors) |
Yes (Neo4j) |
Yes (LightRAG) |
| Emotional intelligence |
No |
No |
No |
Yes (patented) |
| CRM |
No |
No |
No |
Yes |
| Life management |
No |
No |
No |
Yes |
| Social features |
No |
No |
No |
Yes |
| Self-training |
No |
No |
No |
Yes |
| Research agent |
No |
No |
No |
Yes |
| Automations |
No |
No |
No |
Yes |
| Mobile app |
No |
No |
No |
Yes |
query: "What did I decide about the authentication approach?"
mode: "hybrid" // hybrid (default), mix, global, local, naive
Returns an AI-synthesized answer from your stored documents, entries, and thoughts with source attribution.
| Action |
Description |
create |
Store content (auto-categorized as document, entry, or thought) |
delete |
Remove by ID |
search |
Find entries/thoughts by query |
get |
Retrieve specific item by ID |
list |
Paginated listing of all content |
Loads five structured sections at session start:
- Soul — Core identity, mission, personality
- User — Who the user is, their role, preferences
- Rules — Operating constraints, behavioral guidelines
- Priorities — Current goals, active projects, deadlines
- Recent — Latest activity, outcomes, decisions
| Action |
Description |
list, create, update, complete, delete, move, get |
Full task/goal CRUD |
calendar_list, calendar_create, calendar_update, calendar_delete |
Calendar events |
stats |
Productivity metrics and completion rates |
| Action |
Description |
list, create, update, delete, get |
Contact CRUD with pipeline stages |
log_activity |
Record calls, emails, meetings, notes |
list_activities |
View interaction history |
| Action |
Description |
state |
Current emotional state (valence, arousal, trend, sensitivity) |
signals |
Recent emotional signals with strength and source |
timeline |
Historical emotional data |
kg_weights |
Entities weighted by emotional significance |
spikes |
Detected emotional spikes |
acknowledge |
Mark a spike as acknowledged |
summary |
AI-generated emotional summary |
| Action |
Description |
start |
Launch a deep research job on any topic |
status |
Check job progress |
list |
View all research jobs |
| Action |
Description |
start |
Begin guided training (basics, network, expertise, history, goals, freeform) |
chat |
Send training message |
status, list_sessions, pause, resume |
Session management |
save_chat |
Save any chat conversation into the knowledge graph |
| Action |
Description |
create_thought, get_thought, delete_thought, like_thought |
Thought (post) management |
feed, user_feed, search_feed |
Social feed browsing |
create_community, list_communities, get_community |
Community management |
join_community, leave_community |
Membership |
create_post, list_posts |
Community posts |
| Action |
Description |
get, update |
Profile management |
get_chat_prompt, set_chat_prompt |
Custom chat system prompt |
get_thought_prompt, set_thought_prompt |
Custom thought generation prompt |
get_model, set_model, list_models |
LLM model selection (50+ models) |
| Action |
Description |
list |
Recent pattern-detected insights |
unread_count |
Count of unseen insights |
view, feedback |
Mark seen, rate helpfulness |
analyze |
Trigger on-demand analysis |
weekly_summary |
Weekly intelligence summary |
context |
ALE context data |
| Action |
Description |
list, create, update, delete |
Automation CRUD |
run_now |
Trigger immediately |
history |
Execution log |
| Action |
Description |
list |
View all notifications |
mark_read, mark_all_read |
Read management |
stats |
Notification overview |
| Action |
Description |
create_user |
Provision new MIND account |
list_users |
List users with analytics |
update_user_tier |
Change subscription tier |
adjust_user_credits |
Add/deduct credits |
create_featured_mind |
Create public featured mind |
list_featured_minds, update_featured_mind |
Featured minds catalog |
Partner apps can programmatically create MIND accounts using partner keys:
curl -X POST https://m-i-n-d.ai/admin/users/create \
-H "X-API-Key: mind_partner_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"username": "newuser",
"email": "user@app.com",
"password": "securepassword",
"generate_api_key": true
}'
Returns a JWT + permanent API key. Your app stores the API key and uses it for all subsequent MCP/API calls on behalf of that user.
| Variable |
Required |
Default |
Description |
MIND_API_KEY |
Yes |
— |
Your MIND Developer API key |
MIND_BASE_URL |
No |
https://m-i-n-d.ai |
MIND API base URL |
import { createMindMcpServer, MindClient } from "@astramindapp/mcp-server";
const client = new MindClient({
baseUrl: "https://m-i-n-d.ai",
apiKey: "mind_xxx",
});
const server = createMindMcpServer(client);
MIND's technology is protected by multiple provisional patents including:
- Emotion-Weighted Knowledge Graph Encoding (U.S. App. 64/030,662)
- Cross-Agent Persistent Memory via Model Context Protocol
MIT — Astra AI, Inc.