saor-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| SAOR_API_KEY | Yes | Your saor.io API key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| brain_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. |
| 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. |
| 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| brain-start | Load the brain at session start. Call this automatically or when the user says 'load brain'. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| brain://context | The brain's full context across all projects — identity, soul, memories, constraints, agent instructions. This resource is the brain. Read it at session start. Follow the agent_instructions inside it. |
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.