Governed MCP workflows with policy validation, findings tracking, and review gates.
MCP Server: io.github.aryaminus/controlkeel
This MCP server provides governed MCP workflows featuring policy validation, findings tracking, and review gates. It is designed to support secure and compliant interactions for AI agents and development workflows, aligning actions with policy-as-code practices. The repository is labeled with Elixir and Phoenix.
🛠️ Key Features
Policy validation
Findings tracking
Review gates
🚀 Use Cases
AI governance for agent workflows
Code review process controls
Compliance and compliance-as-code checks
DevSecOps-oriented tooling and security workflows
⚡ Developer Benefits
Better observability of policy-related outcomes via findings tracking
Clear enforcement points through review gates
Integration fit for Model Context Protocol (MCP) tooling ecosystems
⚠️ Limitations
Details beyond governance components (e.g., specific tools, endpoints, or configuration) are not provided in the available data.
Turn the way your team works into enforceable memory for AI agents. - @arya_minus
ControlKeel is an agent control plane for day-to-day governed engineering. Through observation, findings and evaluation, it learns your intent rules, review taste and delivery habits, turning them into typed memory, policy checks and proof bundles. CK sits between your coding agents and production as a portable "company brain": comparing intended delivery against actual delivery and turning raw agent intent into policy-validated tasks.
If you're using an AI agent today, you probably have an *.md telling it how to behave. But a rules/specs file is just a promise made to the model. ControlKeel enforces the output. Beyond just catching bugs, CK solves the "Unknown Unknowns" problem: having to re-explain your domain knowledge in every single session.
Product loop
Capture intent and policy — scope, risk, budget, domain pack, and human taste become CK state.
Validate agent output — deterministic checks and optional advisory review produce findings before risky work reaches main.
Gate only when needed — humans approve high-impact actions when intent, risk, or policy requires it.
Improve with evals — traces and recurring failures become bounded regression evidence for specific suites and subjects.
The operating rule is simple: spend tokens on discovery, not rediscovery.
Recurring behavior moves through a human-gated deterministic promotion path;
once its regression evidence passes, checks, APIs, CLIs, or workflows handle
the known case and agents handle only exceptions and genuinely new uncertainty.
ControlKeel transforms your domain knowledge from "raw" intent and "shelfware" documentation into a living system that remembers, enforces, and evolves.
Quick start
One-line setup via your agent
Copy/paste this into your agent (OpenCode, Codex, Claude, or another supported host):
text
Set up ControlKeel for this repository. Read and follow https://raw.githubusercontent.com/aryaminus/controlkeel/main/README.md, https://raw.githubusercontent.com/aryaminus/controlkeel/main/docs/getting-started.md, https://raw.githubusercontent.com/aryaminus/controlkeel/main/docs/support-matrix.md, and https://raw.githubusercontent.com/aryaminus/controlkeel/main/docs/agent-integrations.md. Install ControlKeel if missing, run `controlkeel setup`, detect this agent host, attach the strongest supported path with `controlkeel attach <host>`, then run `controlkeel attach doctor`, `controlkeel provider doctor`, `controlkeel status`, `controlkeel findings`, and the host-native MCP check. If CK is available only as MCP, call `ck_attach` for this host. Apply only safe local fixes and redact secrets from logs. Pause and ask before continuing if the host needs workspace trust, manual provider configuration, a restart after attach/plugin changes, or a plan-review approval that cannot auto-wait. Ensure the project is trusted and restart the host after attach/plugin changes.
CLI install
Install the CLI:
bash
brew tap aryaminus/controlkeel && brew install controlkeel
# or
npm i -g @aryaminus/controlkeel
# or
curl -fsSL https://github.com/aryaminus/controlkeel/releases/latest/download/install.sh | sh
controlkeel
controlkeel setup
controlkeel attach opencode # project scope by default; use another supported host as needed
controlkeel attach doctor
controlkeel provider doctor
controlkeel status
controlkeel findings
Run setup and attach from the repository you want to govern. Project scope writes host files only inside that repository. --scope user writes host-level files under your user configuration only for targets that explicitly support user scope; it does not turn project binding or proof state into global state.
ControlKeel includes a persisted benchmark engine. Current user-facing evidence is bounded to the named suite, subject, and scoring definition below; docs/benchmarks.md is the canonical reference for full tables, caveats, JSON exports, and agent-host protocols.
Risky suite host_comparison_v1: null_policy_baseline caught 0/12; controlkeel_validate caught 12/12, blocked 9/12, and hit expected rules 9/12 with median deterministic validation time 52 ms, 0 provider tokens.
Paired benign suite benign_baseline_v1: controlkeel_validate produced 0/10 catches, 0/10 blocks, FPR 0.000, median deterministic validation time 42 ms, 0 provider tokens.
Read the numbers precisely: deterministic scanner evidence is not the same as model-backed agent-host evidence. Reproduction commands and the OpenCode/Copilot/Claude/Codex comparison protocol live in docs/benchmarks.md.
What ships today
Local governance: CLI, the full local stdio MCP tool set, project binding, host attach/export bundles, scanner validation, findings, reviews, proof bundles, budgets, and typed memory.
Host and runtime support: native attach for supported hosts, runtime exports for headless/outer-loop systems, a narrower OAuth-scoped hosted MCP set, minimal A2A, and fallback validation/proxy paths.
Team/project operations: org membership, invitations, workspace GitHub repo bindings, service accounts, webhooks, workspace tool policy, and policy-set APIs.
Cloud evidence paths: opt-in cloud telemetry, workspace keys, cloud run packages, runtime callbacks, and dormant-until-configured bidirectional sync for findings, reviews, digests, and memory records.
Observability loop: timelines, memory quality, costs, trends, problem clusters, eval candidates, benchmark drafts/history, and promotion advisories.
