Give your AI agent
terminal memory.
11 tools and 5 resources that let Claude Code, Cursor, and other AI agents query your shell history directly. 100% local. Zero config.
suv init claude-code or suv init cursor for Cursor
Your AI agent is flying blind.
Every time your AI coding agent runs a shell command, it forgets the result. It can't see what failed five minutes ago. It can't check if a command is risky before running it. It has no memory of your terminal history.
MCP (Model Context Protocol) changes that. It's the open standard that lets AI agents access structured data sources. Suvadu is an MCP server that turns your shell history into a queryable knowledge base your agent can access in real time.
No cloud. No API keys. No config files to edit. Just run one command and your agent gets terminal memory.
11 tools your agent can call.
Every tool reads from your local SQLite database. No network, no latency, no permissions to configure.
Discover
search_commands Search history by text, directory, executor, and date range
recent_commands What just happened in this directory
command_status Has this command been run before? What happened?
Analyze
get_stats Command counts, success rates, activity patterns
what_changed File-modifying operations: writes, deletes, moves
what_failed Failed commands and the prompt that caused them
Predict
suggest_next Frecency-based prediction of your next command
assess_risk Pre-execution safety check with risk classification
Context
session_history Full chronological history of a session
list_sessions Browse sessions with metadata
get_prompts AI agent prompts and the commands they triggered
Context before the first prompt.
5 resources are automatically injected into your agent's context at session start. No tool calls needed.
Most MCP servers wait for the agent to call a tool. Suvadu goes further — it pre-loads context so your agent already knows what's happening in your terminal before you even ask.
suvadu://history/recent Last 20 commands with exit codes suvadu://failures/recent Recent failures grouped by prompt suvadu://stats/today Today's command count & success rate suvadu://risk/summary Risk summary of recent agent commands suvadu://agents/activity Per-agent activity breakdown Set up in one command.
No config files. No API keys. No network ports.
Install Suvadu
curl -fsSL https://downloads.appachi.tech/suvadu/install.sh | sh Connect your agent
suv init claude-code or suv init cursor
Done. Agent has memory.
Start coding. Your AI agent can now search history, check failures, and assess risk.
Works with every major AI coding agent.
One-command MCP setup for Claude Code and Cursor. Command tracking for everything else.
MCP auto-configured
Commands tracked automatically
MCP can be manually configured for any agent that supports the Model Context Protocol.
What it looks like in practice.
Real queries your AI agent can answer with Suvadu's MCP tools.
What failed in this project in the last hour?
called what_failed
3 commands failed: npm test (exit 1, 23 min ago), cargo build (exit 101, 45 min ago), docker compose up (exit 137, 58 min ago). The npm test failure was triggered by your prompt "fix the auth middleware".
Is it safe to run git push --force origin main?
called assess_risk
Risk level: Critical. This command has been run 0 times in this project. Force-pushing to main is destructive and irreversible. I recommend git push --force-with-lease instead.
100% local. By design.
Your shell history never leaves your machine.
No network ports
JSON-RPC over stdin/stdout only. Never opens a TCP or UDP port.
No cloud
All data stays in your local SQLite database. No accounts. No telemetry.
Same data as CLI
Reads from the same database that powers suv search. No separate data store.
Secret redaction
API keys, tokens, and passwords are auto-redacted before they touch disk.
Ready to give your agent memory?
Install Suvadu, run one init command, and your AI agent gets terminal context forever. For the complete reference, see the MCP Server documentation.
Get started in 30 seconds
Three commands. That's it.
curl -fsSL https://downloads.appachi.tech/suvadu/install.sh | sh echo 'eval "$(suv init zsh)"' >> ~/.zshrc source ~/.zshrc && suv status
Also available via brew tap AppachiTech/suvadu && brew install suvadu,
cargo install suvadu, and
manual install.