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Cursor is an AI-first code editor built for pair programming with AI. With the Orq MCP integration, access the Orq.ai workspace directly from Cursor’s chat interface and coding environment.

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Prerequisites

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Installation

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Add MCP Server

  1. Open Cursor Settings by clicking Cursor → Settings in the top-left menu
  2. Click Tools & MCP in the sidebar
  3. Click New MCP Server to open an empty mcp.json editor
  4. Paste the following configuration:
{
  "mcpServers": {
    "orq": {
      "url": "https://my.orq.ai/v2/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_ORQ_API_KEY"
      }
    }
  }
}
  1. Replace YOUR_ORQ_API_KEY with your actual API key from Workspace Settings → API Keys
  2. Save the configuration
The Orq MCP server should automatically connect and show a green indicator. All Orq.ai tools will be available immediately.

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Verification

Open Cursor’s chat panel and ask: 
Can you list the available models from Orq?
If the integration is working, a list of AI models from the Orq.ai workspace will appear.
Cursor IDE showing the Orq MCP server connected with a green indicator in the Tools and MCP settings panel

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Available Commands

Use natural language to ask Cursor to perform these operations:
  • Create an agent with custom instructions and tools
  • Get agent configuration for [agent-key]
  • Update agent [agent-key] with new instructions or model
  • Configure agent with evaluators and guardrails
  • Invoke agent [agent-key] with input [message]
  • Retrieve agent response [response-id]
  • Create a deployment called [deployment-key]
  • Get deployment configuration for [deployment-key]
  • Create a skill called [skill-key]
  • List all skills in my workspace
  • Get skill [skill-key]
  • Update skill [skill-key]
  • Delete skill [skill-key]
  • Get analytics overview for my workspace
  • Show me workspace metrics for the last 7 days
  • Query analytics filtered by deployment ID
  • Create a dataset called "customer-queries"
  • List all datapoints in dataset [dataset-key]
  • Add datapoints to dataset [dataset-key]
  • Update datapoint [datapoint-id]
  • Delete specific datapoints in dataset [dataset-key]
  • Delete dataset [dataset-key]
  • Create an experiment from dataset [dataset-key]
  • List all experiment runs
  • Export experiment run [run-id] as CSV
  • Run experiment and auto-evaluate results
  • Get evaluator configuration for [evaluator-key]
  • Create an LLM-as-a-Judge evaluator for tone
  • Create a Python evaluator to check response length
  • Add evaluator to experiment [experiment-key]
  • Update evaluator [evaluator-key] with a new prompt
  • Update Python evaluator [evaluator-key] with revised code
  • List traces from the last 24 hours
  • Show me traces with errors
  • Get span details for trace [trace-id]
  • Find the slowest traces from today
  • Show all traces for thread [thread-id]
  • List all available chat models
  • List all available embedding models
  • Invoke model [model-id] with prompt [message]
  • Search for datasets named "customer"
  • Find experiments in project [project-id]
  • List directories in project [project-id]
  • Search the Orq.ai docs for [topic]
  • Delete agent [agent-key]
  • Delete experiment [experiment-key]
  • Delete evaluator [evaluator-key]
  • Delete prompt [prompt-key]
  • Delete knowledge base [knowledge-base-key]
Use delete_dataset to delete a dataset along with all its datapoints.
See the MCP Quickstart for the full tool reference and examples.

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Troubleshooting

  1. Check Cursor’s Orq MCP status in Settings
  2. Verify the API key is correct
  3. Restart Cursor
  4. Check the Developer Console (Help > Toggle Developer Tools)
  1. Confirm the API key is valid
  2. Ensure the API key has the necessary permissions
  3. Try regenerating the API key
  1. Verify the Orq MCP server is running in Settings
  2. Check network connectivity
  3. Review any error messages in the Developer Console

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Skills

Skills add pre-built agentic workflows to Cursor for the full Build, Evaluate, Optimize lifecycle. See the Skills page for the full reference.

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Installation

# 1. Open Cursor Settings → Rules
# 2. In Project Rules, click Add Rule
# 3. Select Remote Rule (Github)
# 4. Enter: orq-ai/assistant-plugins

npx skills add orq-ai/assistant-plugins

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Available Skills

Triggered by describing what you need. Cursor picks the right skill automatically.
SkillDescription
build-agentDesign, create, and configure an Orq.ai agent
build-evaluatorCreate validated LLM-as-a-Judge evaluators
analyze-trace-failuresRead production traces and categorize failures
run-experimentCreate and run experiments with evaluation
generate-synthetic-datasetGenerate and curate evaluation datasets
optimize-promptAnalyze and optimize system prompts
setup-observabilityInstrument LLM applications with orq.ai tracing: AI Gateway for zero-code traces, or OpenTelemetry for framework-level spans
compare-agentsRun cross-framework agent comparisons using evaluatorq
Slash commands (/orq:quickstart, /orq:traces, etc.) are only available in Claude Code. See Skills for details.