Tuning Engines Governed AI Runtime
Govern models, agents, skills, and other MCP calls with policy, approvals, traces, and usage analytics from Tuning Engines.
Overview
Tuning Engines (cerebrixos-org) is a MCP server for the Operate phase that governs model, agent, skill, and MCP workflows with policy, approvals, traces, and usage analytics.
What is this MCP server?
- Policy and approval gates across model, agent, skill, and MCP workflow paths
- Execution traces and usage analytics for who invoked what and when
- tuningengines-cli mcp serve subcommand (npm 0.4.17) over stdio MCP
- TE_API_KEY from tuningengines.com or te auth login for hosted runtime access
- Registry version 0.4.17 for io.github.cerebrixos-org/tuning-engines
- CLI invoked as tuningengines-cli with two positional args: mcp and serve
- Single required secret environment variable TE_API_KEY
Community signal: 2 GitHub stars.
What problem does it solve?
Scattered agent tools make it hard to enforce who can run which model or MCP action and to see usage after you ship.
Who is it for?
Indie operators running several MCP skills who want a hosted governance layer without building their own audit stack.
Skip if: Builders who only need a single local skill with no approvals, tracing, or shared API key management.
What do I get? / Deliverables
After you connect tuningengines-cli with TE_API_KEY, agent calls can flow through governed policies with approvals and traceable analytics.
- Policy-governed MCP access to models, agents, skills, and related workflows
- Approval workflows where your TE account rules require them
- Traces and usage analytics surfaced through the Tuning Engines runtime
Recommended MCP Servers
Journey fit
Ongoing governance and observability sit in Operate once agents and skills are live and you need policy enforcement and audit trails. Monitoring is the canonical shelf because traces and usage analytics are the day-two control plane, not the initial feature code.
How it compares
Governed AI runtime MCP control plane, not a fine-tuning-only API or a single-purpose integration.
Common Questions / FAQ
Who is Tuning Engines Governed AI Runtime for?
Solo founders and small teams operating multiple AI agents, skills, and MCP servers who need policy, approvals, and usage visibility.
When should I use Tuning Engines Governed AI Runtime?
Use it in Operate when live agent workflows need enforced rules, approval steps, and trace or analytics review.
How do I add Tuning Engines Governed AI Runtime to my agent?
Install tuningengines-cli, run mcp serve over stdio in your MCP config, set required TE_API_KEY from tuningengines.com or te auth login, then restart your agent.