About Openlayer
Openlayer Skills helps coding agents integrate applications with Openlayer, an AI evaluation and observability platform. It provides current guidance for tracing, evaluations, tests, guardrails, CI/CD gating, and debugging through Openlayer’s MCP server.
Key features
- 01Instrument tracing and monitoring
- 02Configure offline evaluations
- 03Create tests and guardrails
- 04Gate CI/CD with evaluation results
- 05Debug failing tests with MCP
- 06Query projects and evaluation data
Use cases
- 01Add tracing to an LLM application
- 02Set up offline evaluation files
- 03Block CI/CD on evaluation results
- 04Investigate failing tests in Openlayer
MCP support
Connects agents to the hosted Openlayer MCP server. Agents can read workspace projects, traces, tests, results, and governance rules, and create or update them; write and delete actions require confirmation.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.openlayer.com/mcpclaude mcp add --transport http com-openlayer https://mcp.openlayer.com/mcp{"mcpServers":{"com-openlayer":{"type":"http","url":"https://mcp.openlayer.com/mcp"}}}Open in Cursor{"servers":{"com-openlayer":{"type":"http","url":"https://mcp.openlayer.com/mcp"}}}Open in VS Code{"mcpServers":{"com-openlayer":{"type":"http","url":"https://mcp.openlayer.com/mcp"}}}{"mcpServers":{"com-openlayer":{"serverUrl":"https://mcp.openlayer.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEOpenlayer Skills
Agent Skills that teach AI coding assistants (Claude Code, Cursor, GitHub Copilot, etc.) how to integrate apps with Openlayer — the AI evaluation and observability platform.
Coding agents produce significantly better results with the skill installed, because they are conditioned to follow Openlayer's current best practices instead of guessing from memory (outdated SDK calls, wrong env vars, invented test configs).
Skills
| Skill | Description |
|---|---|
| openlayer | Main skill. Instrument code with tracing (monitoring), set up offline openlayer.json + tests.json evals (development), create tests/guardrails, gate CI/CD, run the MCP fix-loop, and access data and docs. |
Installation
Claude Code plugin
claude plugin marketplace add openlayer-ai/openlayer-skills
claude plugin install openlayer@openlayer
Cursor plugin
/add-plugin openlayer
GitHub Copilot
GitHub Copilot reads .github/plugin/plugin.json and the same skills and .mcp.json as Claude Code.
In VS Code, add the marketplace to your settings.json, then install Openlayer from the Extensions view (search @agentPlugins):
{
"chat.plugins.marketplaces": ["openlayer-ai/openlayer-skills"]
}
In the Copilot CLI:
copilot plugin marketplace add openlayer-ai/openlayer-skills
copilot plugin install openlayer@openlayer
Other agents
Codex, Gemini CLI, Windsurf, opencode, and dozens more install through the skills CLI:
npx skills add openlayer-ai/openlayer-skills --skill "openlayer"
For claude.ai, Copilot's cloud coding agent and code review, a manual symlink, or anything else, see the installation guide — it is the source of truth and stays current as these tools change.
Prerequisites
An Openlayer account and an API key:
export OPENLAYER_API_KEY=...
# self-hosted / local only:
export OPENLAYER_BASE_URL=https://api.openlayer.com/v1
Find your API key in the Openlayer app under Workspace settings → API keys (see https://docs.openlayer.com/workspace-and-projects/find-your-api-key).
Usage
Once installed, the agent uses the skill automatically when relevant — for example:
- Adding Openlayer tracing/monitoring to an LLM or agent app
- Setting up offline evals (
openlayer.json+tests.json) and pushing commits - Creating tests from the catalog and configuring guardrails
- Gating CI/CD on eval results
- Debugging failing tests via the Openlayer MCP fix-loop
- Querying projects, pipelines, test results, or looking up Openlayer docs
MCP server
The plugin also connects your agent to the hosted Openlayer MCP server at https://mcp.openlayer.com/mcp (US accounts on app.openlayer.com; this is the .mcp.json default). The first time a tool is used, you sign in to Openlayer with OAuth and pick a workspace. The agent can then read projects, data sources, traces, tests, results, and governance rules in that workspace, and create or update them when you ask. Write and delete tools ask for confirmation.
EU accounts (eu.openlayer.com) must use https://mcp-eu.openlayer.com/mcp. Each hosted MCP server only accepts tokens from its own region's API, so an EU account pointed at the US server fails OAuth. Outside the regional cloud hosts the default stays https://mcp.openlayer.com/mcp.
Tool calls go only to Openlayer's API. Nothing is sent anywhere else. See the privacy policy. Self hosted deployments should point the server at their own URL, as the MCP docs describe.
Feedback & Requests
Something not working as expected, or want a new use case covered? Open an issue at https://github.com/openlayer-ai/openlayer-skills/issues or email [email protected].
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.