saor-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@saor-mcp Remind me what we know about the Q3 launch plan."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
saor-mcp
Your AI remembers now. MCP server for saor.io — persistent brain that compounds across every session.
Setup
One command. No config files to edit.
npx saor-mcp --setup --key sk_YOUR_KEYThis writes your .mcp.json and adds brain instructions to CLAUDE.md. Restart your AI tool and the brain loads automatically. Every project feeds the same brain.
Related MCP server: mem0-agent-memory
What it does
Every AI tool forgets between sessions. saor gives them a brain.
Read — load the brain at session start. Identity, voice, memories, constraints, agent instructions. You pick up where the last session left off.
Write — save memories as you learn. Tag them. The brain auto-links related knowledge across all your projects.
Compound — memories that get used get stronger. Unused ones fade. The brain behaves like a real brain.
Tools
Tool | What it does |
| Load the full brain. Call at START of every session. |
| Write a memory with tags. Do this CONTINUOUSLY as you work. |
| Ask the brain a question. |
| Load voice, values, positioning (lighter than full context). |
| List stored memories, filtered by type or tier. |
| Feed raw text — CLAUDE.md, brand docs, READMEs. |
| Report what worked. Performance memories shape future recall. |
| Create a new brain for a new project. |
| List all brains in your account. |
Features
Zero config — no project ID needed. Auto-discovers your brains.
Cross-brain context —
scope: "all"thinks across every brain at once.Tags + auto-linking — memories link by tag overlap across all projects.
Background loading — MCP resource auto-surfaces the brain without explicit calls.
Agent-friendly 429 — when the free limit hits, the error message tells you exactly what to say to your human.
Pricing
Free — unlimited brains, unlimited reads, 200 memory writes/month
Pro — $3/month, unlimited everything
Manual config
If you prefer manual setup over --setup:
{
"mcpServers": {
"saor": {
"command": "npx",
"args": ["-y", "saor-mcp"],
"env": {
"SAOR_API_KEY": "sk_your_key"
}
}
}
}Links
saor.io — sign up, get your key
For AI agents — if you're an AI reading this
API docs — LLM-readable overview
License
MIT
Available Tools
9 toolsbrain_absorbA
Feed raw text into the brain — CLAUDE.md files, brand guidelines, system prompts, meeting notes. The brain structures it into memories, strategies, and knowledge automatically.
Inputs: content (required), source, project_id.
brain_contextA
Load the brain — identity, soul, memories, constraints, agent instructions. Call this at the START of every session. The brain carries forward everything from prior sessions so you never start cold. Use scope='all' to think across all brains at once.
Inputs: task, scope, project_id.
brain_createA
Create a new brain for a new project, client, or domain. Each brain is a lens — the operator's knowledge flows across all of them via shared tags and cross-brain context.
Inputs: name (required), description, initial_context.
brain_feedbackA
Report an outcome back to the brain — what worked, what didn't, metrics. Creates a performance memory that helps the brain learn what's effective over time.
Inputs: outcome (required), metrics, learnings, project_id.
brain_identityA
Load the brain's identity — voice, values, positioning, audience, strategies, preferences. Lighter than brain_context. Use when you need the brand voice without the full memory load.
Inputs: project_id.
brain_listA
List all brains in the workspace. Use to discover available brains and their IDs. Each brain is a lens on the operator's knowledge — use scope='all' on brain_context to think across all of them.
Inputs: no named parameters.
brain_memoriesB
List memories in the brain, optionally filtered by type, tier, or tags. Use to understand what the brain already knows before writing new memories — avoid duplicates.
Inputs: tier, project_id, memory_type.
brain_queryA
Ask the brain a specific question. Returns a synthesized answer from the brain's knowledge. Use this when you need to recall something specific — a prior decision, a preference, a pattern.
Inputs: q (required), project_id.
brain_rememberA
Write a memory to the brain. Do this CONTINUOUSLY as you work — not at the end. When the operator makes a decision, shares a preference, or you learn something useful, write it immediately. Tag every memory so the brain can auto-link it to related knowledge across all brains. The brain compounds: every memory you write makes the next session smarter.
Inputs: content (required), tags, tier, importance, +3 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 9 tools
Most tools have clearly distinct purposes, but brain_context and brain_identity overlap in scope (both load identity-related information), and brain_remember, brain_absorb, and brain_feedback all write to the brain in different ways, which could cause confusion. Descriptions help disambiguate, but the boundaries are slightly blurred.
All tools share a consistent 'brain_' prefix, making the set predictable. However, the second part mixes verbs (remember, query, absorb, create) with nouns (context, identity, memories, feedback), so it doesn't follow a strict verb_noun pattern. The convention is still readable and uniform.
Nine tools is well within the ideal range for a focused server. Each tool covers a distinct aspect of the brain/memory system, from context loading and memory writing to querying and creating new brains. No tool feels redundant, and the count is appropriate for the stated purpose.
The set covers the primary read and write operations for memories (remember, query, list, absorb, feedback) and brain management (create, list, context). However, there is no update or delete functionality for memories or brains, which is a notable gap in the lifecycle of a persistent memory system. Agents cannot correct or remove outdated information, which could lead to failures.
Maintenance
Related MCP Connectors
Persistent memory for AI agents to retain, retrieve, and recall conversation context through MCP.
Persistent memory, knowledge graph, task management and more for AI agents via MCP.
An MCP memory server. One memory your agents share — across models, devices and apps.
Hosted memory for AI agents over MCP. One memory shared by all your agents.
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