Your codebase context.
Always alive. Always ready.
Every agent session starts blind. Before it's useful, someone has to manually reload context — re-reading files, re-explaining architecture, re-establishing what's been tried. That's 10–15 minutes of overhead, every session, every engineer, every day.
Parker is built to solve that. We ingest your GitHub, Slack, and Linear and build a persistent model of your codebase and team — so agents start with context instead of asking for it.
— explain payment conventions
— who reviewed PR #847?
— what changed last week?
withRetry()npm install -g @parker/cliWorks with Claude Code, Cursor, and any MCP-compatible agent
The problem
The problem has two layers.
AI made your engineers more capable. It didn't solve the context problem — it made it worse.
Every agent session starts from zero.
You've invested in Cursor and Claude Code. Your engineers are running sessions all day — maybe 4 or 5 at a time. The agents are capable. But every session starts blind.
- ✕10–15 min reloading context before every session
- ✕CLAUDE.md maintained by hand — stale within days
- ✕20 engineers × 10 sessions/day = thousands of dead minutes
- ✕Agent guesses at ownership, conventions, and recent changes
Your team knowledge lives in one person's head.
Past 15 engineers, the mental model a great CTO carries — who owns what, who built it and why, who to pull at 2am — stops fitting. When someone leaves, you find out three weeks later what they knew.
- ✕Key engineer leaves — critical knowledge evaporates
- ✕New hires spend months on Slack archaeology
- ✕Staffing decisions default to gut feel, not expertise signals
- ✕Incidents: “Who built this service again?”
AI coding tools are making both problems worse. Engineers are shipping more code faster, written by fewer people who fully understand it. The gap between what the code does and what the humans around it know is widening.
Agent integration
The context layer your agents query directly
Parker runs as an MCP server your agents call mid-session — not a dashboard you check after. One command to connect Claude Code, Cursor, or any MCP-compatible agent.
parker contextOwner, conventions, recent changes, and active reviewers for any file or directory. Your agent writes code that fits before it writes a line.
parker whoNeed to touch the rate limiter? Parker surfaces who actually owns that code — with evidence from git, Slack, and Linear — not just whoever's top of mind.
parker reviewFeedback based on the conventions of the engineers who own that area. Catches the issues a generic linter never would.
parker mcpProfiles are rebuilt from live GitHub, Slack, and Linear activity. No markdown files to keep in sync. No manual updates. Context stays grounded in what's actually happening.
1. Install
Set up the CLI
One command. Works on macOS and Linux, with any git-based project.
Prefer npm? npm install -g @parker/cli
2. Connect
Works with any agent
MCP server for Claude Code and Cursor today. Context also available as CLI commands and auto-generated context files — no matter how your agents consume it.
As your team scales
The same layer. A second kind of value.
The intelligence that makes your agents faster is the same intelligence that keeps your org from flying blind as you grow. Same data pipeline. Same profiles. One layer that compounds.
The context that made your agent productive — who owns the payments module, what conventions exist, what's changed and why — is exactly the context that used to live in the CTO's head. Parker makes it persistent, queryable, and available to everyone on the team.
parker who kafkaRight person, with citations.
Not whoever's top of mind — the engineer with 142 commits to kafka/, active in #kafka-platform, who authored KafkaProducer.
parker weekStatus update from your actual activity.
Commits, PRs merged, reviews given, tickets closed — reconstructed automatically. Ready to paste into your 1:1 or standup.
parker prep 1on1Talking points before you walk in.
Wins, in-progress work, and the patterns worth discussing — pulled from real activity, not from memory.
Knowledge distribution
Bus factor risks, review load, and who actually knows what — surfaced weekly so you're never surprised by a departure.
Onboarding that actually works
New engineers arrive to a team model that's already accurate — who to ask, what conventions exist, why things are built the way they are.
Expertise signals when you need them
Staffing decisions grounded in who has the relevant depth — not just who's available or loudest in the room.
See it in action
What your agents — and engineers — can query
Parker exposes team intelligence through an MCP server your agents call directly, and a CLI for engineers. The same context, available everywhere.