raggy-mcp
Click on "Install 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., "@raggy-mcprecall what we worked on yesterday"
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.
raggy-mcp
MCP server that gives every AI agent a universal brain -- centralized memory and knowledge via Raggy. One brain, all agents.
How agents use Raggy
Starting in 0.3.0, raggy-mcp ships with a built-in agent protocol that
teaches any connected client how to use the memory tools correctly. The
protocol is advertised through the MCP instructions field on initialization,
so compatible clients (Claude Desktop, Cursor, Zed, Windsurf, Claude Code, and
most modern MCP editors) pass it to the underlying LLM automatically.
You no longer need to paste memory rules into SOUL.md, AGENTS.md, or CLAUDE.md -- connect the server and every agent knows the rules:
Recall at the start of every session (once), via
raggy_contextAuto-capture decisions, errors, preferences, insights as they happen
Link related memories into a knowledge graph with
raggy_linkUse
raggy_timelinefor "what did we do today" andraggy_threadsfor "what was in my last session"Respect "forget that" / "don't save that" immediately
See PROTOCOL.md for the full text, and for manual install
instructions if your client doesn't yet support MCP instructions.
Related MCP server: Mnemexa MCP
Features
Universal memory protocol: Auto-loaded agent rules via MCP
instructionsCapture & Recall: Store decisions, errors, insights, snippets, research, and bookmarks that persist across sessions and agents
Context bootstrap:
raggy_contextloads relevant prior-session memories at the start of every conversationTimeline & Threads: Chronological memory and per-session grouping for temporal queries
Knowledge graph: Explicit links between memories (
caused_by,resolved_by,supersedes,refines,contradicts,related_to,follows_from,part_of)Private sources: Upload files, URLs, and long-form content as searchable private knowledge
Forget: Remove outdated or redact-while-preserving memories when they are no longer needed
Installation
Using npx (recommended)
Add to your Claude Code configuration:
{
"mcpServers": {
"raggy": {
"command": "npx",
"args": ["-y", "raggy-mcp"]
}
}
}Manual installation
npm install -g raggy-mcpThen add to your Claude Code configuration:
{
"mcpServers": {
"raggy": {
"command": "raggy-mcp"
}
}
}Configuration
API Key (optional)
For Pro tier access (200 searches/day), set your API key:
# Via environment variable
export RAGGY_API_KEY=rgy_live_xxxxx
# Or create config file
mkdir -p ~/.claude/raggy
echo '{"apiKey": "rgy_live_xxxxx"}' > ~/.claude/raggy/config.jsonFree tier (20 searches/day) works without an API key.
Tools
All tools follow the agent protocol loaded automatically at connect time (see PROTOCOL.md).
Memory writing
raggy_capture-- Structured auto-capture with rich metadata. Use for decisions, errors, preferences (tag as["preference"]), insights, snippets, and research. Requirescontent_typeandimportance.raggy_remember-- Simple unstructured note. Preferraggy_capturewhen you have a clear type.raggy_link-- Connect two memories in the knowledge graph using one of:caused_by,resolved_by,supersedes,refines,contradicts,related_to,follows_from,part_of.raggy_forget-- Delete or redact a memory. Call when the user says "forget that" or "don't save that".
Memory reading
raggy_context-- Mandatory first action of every session. Loads relevant memories from prior sessions based on project/technologies/query.raggy_recall-- Targeted semantic search. Use only as a follow-up lookup mid-session; don't call twice per question.raggy_timeline-- Chronological browse. Use for "what did we do today/yesterday/last week" questions.raggy_threads-- Session-based browse. Use for "what was in my last session" questions.
Private sources (requires API key)
raggy_upload-- Upload files, URLs, or long-form content as a searchable private source.raggy_private_sources-- List uploaded sources.raggy_delete_source-- Delete an uploaded source by ID.
Pricing
Tier | Searches | Features |
Free | 20/day | Detection, semantic search |
Pro | 200/day | Priority support |
Enterprise | Custom | Private docs, SSO, SLA |
Development
# Install dependencies
npm install
# Build
npm run build
# Run locally
npm startLicense
MIT
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/sonofakel/raggy-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server