Cursor MCP Integration

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Overview

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Prerequisites

• Cursor IDE installed
• Active Orq.ai account
• Orq.ai API key

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Installation

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Add MCP Server

1. Open Cursor Settings (⌘ + , on Mac, Ctrl + , on Windows/Linux)
2. Navigate to Features → Model Context Protocol
3. Add the Orq MCP server configuration:

{
  "mcpServers": {
    "orq": {
      "url": "https://my.orq.ai/v2/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_ORQ_API_KEY"
      }
    }
  }
}

4. Replace YOUR_ORQ_API_KEY with your actual API key
5. Save the configuration

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Verify Installation

list available models

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Available Commands

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Usage Examples

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Chat Panel Commands

create a dataset called "API Tests" with 20 synthetic API request examples

1. Generate 20 synthetic API request examples
2. Use create_dataset to create a new dataset named “API Tests”
3. Use create_datapoints to add all examples to the dataset
4. Confirm creation with the dataset ID

show me errors from the last 24 hours

1. Calculate the time range for the last 24 hours
2. Use list_traces with error status filter
3. Display trace IDs, error messages, and timestamps
4. Provide a summary of error types and frequency

create an experiment comparing GPT-5.2 and Claude Sonnet 4.6 using the "user-queries" dataset

1. Search for the “user-queries” dataset using search_entities
2. Use create_experiment with two configurations (one for GPT-5.2, one for Claude Sonnet 4.6)
3. Run the experiment against all datapoints in the dataset
4. Display the experiment ID and status

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Inline Code Integration

1. Select code in your editor
2. Open chat (⌘ + L)
3. Ask about traces or analytics related to your code

# Select this function
def process_user_query(query):
    response = orq.deployments.invoke(...)
    return response

how has this endpoint performed over the last week?

1. Extract the deployment key from the selected code
2. Use query_analytics with the deployment filter
3. Set time range to the last 7 days
4. Analyze performance metrics (requests, errors, latency, tokens)
5. Provide insights and recommendations based on the data

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Dataset Creation from Code

[
  {"input": "What is AI?", "expected_output": "Artificial Intelligence..."},
  {"input": "Explain ML", "expected_output": "Machine Learning..."}
]

create a dataset from the JSON array above and add it to my workspace

1. Parse the JSON array from your code
2. Use create_dataset to create a new dataset with an auto-generated name
3. Use create_datapoints to add each entry as a datapoint
4. Confirm the dataset ID and number of datapoints added

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Experiment Analysis

create an experiment using "customer-feedback" dataset, configure it with two prompts: one focused on empathy and one focused on brevity, then run it and summarize the results

1. Search for the “customer-feedback” dataset using search_entities
2. Use create_experiment with two prompt variants (empathy-focused and brevity-focused) and auto-run enabled
3. Execute both variants against all datapoints automatically via the auto-run option
4. Use get_experiment_run to retrieve evaluation metrics
5. Compare the two variants and provide a summary of which performed better

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Performance Investigation

find the 5 slowest traces from today and show me their span details

1. Use list_traces with today’s date filter
2. Sort traces by duration (descending)
3. Retrieve the top 5 slowest traces
4. Use list_spans to fetch span information for each trace
5. Display latency breakdowns, bottlenecks, and performance insights

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Synthetic Data Generation

generate 50 realistic customer support questions about a SaaS product and create a dataset called "Support Training Data"

1. Generate 50 synthetic customer support questions and expected responses
2. Use create_dataset to create a dataset named “Support Training Data”
3. Use create_datapoints to add all 50 examples to the dataset
4. Confirm creation with the dataset ID and sample of generated questions

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Troubleshooting