raggy-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., "@raggy-mcp recall 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: Recall
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Forget: Remove outdated or redact-while-preserving memories when they are no longer needed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
Available Tools
11 toolsraggy_captureA
Auto-capture structured memory. Use for: decisions (content_type="decision"), errors+fixes ("error"), preferences ("note" + tags:["preference"]), insights ("insight"), snippets ("snippet"), research ("research"). Set importance: high for decisions/errors/prefs, medium for insights/snippets. Always include source_app + capture_context.agent_id.
Inputs: content (required), url, name, tags, +6 more.
raggy_contextA
MANDATORY FIRST ACTION of every session. Call ONCE before answering the first user message with their initial request as the query. Loads relevant memories from prior sessions. Do not make a second call unless this returned empty and you need a targeted lookup (then use raggy_recall).
Inputs: files, limit, query, project, +3 more.
raggy_delete_sourceA
Delete an uploaded private knowledge base source by ID. Use when the user asks to remove a specific source. For individual memories use raggy_forget instead. Get the ID via raggy_private_sources. Requires API key.
Inputs: sourceId (required).
raggy_forgetA
Call immediately when the user says "forget that", "don't save that", or "delete that memory". Use mode="delete" to remove entirely or mode="redact" to preserve the graph node but strip content. Not for uploaded sources — use raggy_delete_source for those.
Inputs: memory_id (required), mode.
raggy_linkA
Connect two related memories in the knowledge graph. Use when a new memory supersedes/refines/contradicts an old one, or when an error resolves a past bug. Relationship types: caused_by, resolved_by, supersedes, refines, contradicts, related_to, follows_from, part_of.
Inputs: source_id (required), target_id (required), relationship_type (required), note.
raggy_private_sourcesA
List uploaded private knowledge base sources (files/URLs ingested via raggy_upload). Use when the user asks "what sources have I uploaded" or to find a source ID for deletion. Requires API key.
Inputs: no named parameters.
raggy_recallA
Targeted semantic search over memories. Only use AFTER raggy_context at session start has returned nothing relevant, or for a specific follow-up lookup mid-session. Do NOT call twice per question. For time-based queries use raggy_timeline.
Inputs: query (required), tags, limit, source_app.
raggy_rememberA
Save a simple, unstructured note. Prefer raggy_capture for anything with a clear type (decision/error/insight/snippet). Only use raggy_remember for free-form notes where metadata adds no value.
Inputs: content (required), url, name, tags.
raggy_threadsA
Use for session-based queries like "what was in my last session" or "what did we discuss in that conversation about X". action="list" browses recent sessions; action="get" retrieves all memories for one session_id.
Inputs: limit, since, action, session_id, +1 more.
raggy_timelineA
Use for time-based queries like "what did we work on today/yesterday/last week". Pass since as a relative window (e.g. "24h", "7d") or ISO date. Do NOT use raggy_recall for temporal questions — this is the right tool.
Inputs: limit, since, until, session_id, +2 more.
raggy_uploadA
Upload files, URLs, or long-form content as a private knowledge base source. Use for documents, articles, or reference material the user wants indexed and searchable. For short notes/thoughts use raggy_capture instead. Requires API key.
Inputs: name (required), url, content, category.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 11 tools
Tools are largely distinct, but raggy_remember and raggy_capture both create memories, and raggy_context and raggy_recall both handle memory retrieval. Descriptions do separate them well via structured vs unstructured capture and session-start vs targeted lookup, so confusion is unlikely in practice.
All tools share the raggy_ prefix and snake_case formatting, but naming style is mixed: upload, remember, capture, recall, forget, and link are verbs, while context, timeline, threads, and private_sources are noun phrases. There is no consistent verb_noun pattern.
11 tools is well-scoped for a memory/knowledge server covering ingestion, retrieval, deletion, graph linking, and session introspection. Each tool has a clear role and the count feels appropriate rather than bloated or thin.
The set covers upload/list/delete for private sources, capture/remember/forget for memories, context/recall/timeline/threads for retrieval, and link for graph relationships. The main gap is no explicit update/edit operation for a memory, though supersedes links and forget redaction partially address that.
Maintenance
Related MCP Connectors
Persistent memory for AI agents. Search, store, and recall across sessions.
Persistent memory for AI agents. Search and store durable facts, preferences and decisions.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
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