Cursor

Use Cursor as an IDE interface for your Orq.ai workspace. Query traces, run evals, inspect deployments, and debug production LLM behavior without leaving your editor, with the Orq.ai dashboard available for deeper drill‑down.

Cursor

Use Cursor as an IDE interface for your Orq.ai workspace. Query traces, run evals, inspect deployments, and debug production LLM behavior without leaving your editor, with the Orq.ai dashboard available for deeper drill‑down.

MCP

Skills

Natural language

Local-first

Get your API key

View setup docs

Field

Value

Integration type

MCP server (remote Streamable HTTP / SSE)

Setup time

Quick setup once Cursor is installed and MCP is enabled in settings, with an Orq API key configured.

Auth

Orq.ai API key (workspace‑ or project‑level) passed as a bearer token via environment variables or inline config in mcp.json, depending on how you configure the server.

Skills support

Cursor can call Orq MCP tools when the Orq server is registered and enabled in the MCP settings.

Cloud-based

Cursor runs locally as your IDE, while MCP requests from the AI assistant call Orq’s cloud APIs for workspace data.

Multi‑workspace

Define multiple Orq entries in ~/.cursor/mcp.json (or project‑specific .cursor/mcp.json) with different API keys to point Cursor at different Orq workspaces or environments.

Vendor

AnySphere

Pricing

Included with supported Orq.ai workspaces. Cursor’s MCP features are available on supported Cursor plans; confirm availability in both your Cursor and Orq plans

Why Connect Cursor to Orq.ai?

Keep your team in one editor

Stop switching between Cursor, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE your team already uses.

Ask operational questions in natural language

Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Cursor’s AI assistant turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.

Connect evals to your development workflow

Use Orq.ai inside your existing Cursor workflows. From the CLI or desktop app, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.

Keep production behavior visible

Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Cursor brings that visibility into the same environment where you write and review code.

Keep your team in one editor

Stop switching between Cursor, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE your team already uses.

Ask operational questions in natural language

Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Cursor’s AI assistant turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.

Connect evals to your development workflow

Use Orq.ai inside your existing Cursor workflows. From the CLI or desktop app, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.

Keep production behavior visible

Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Cursor brings that visibility into the same environment where you write and review code.

Keep your team in one editor

Stop switching between Cursor, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE your team already uses.

Ask operational questions in natural language

Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Cursor’s AI assistant turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.

Connect evals to your development workflow

Use Orq.ai inside your existing Cursor workflows. From the CLI or desktop app, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.

Keep production behavior visible

Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Cursor brings that visibility into the same environment where you write and review code.

Keep your team in one editor

Stop switching between Cursor, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE your team already uses.

Ask operational questions in natural language

Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Cursor’s AI assistant turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.

Connect evals to your development workflow

Use Orq.ai inside your existing Cursor workflows. From the CLI or desktop app, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.

Keep production behavior visible

Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Cursor brings that visibility into the same environment where you write and review code.

Setup

1: Install Cursor

Install Cursor for your OS and sign in with your account.

Then enable MCP support:

  • Open Cursor Settings (e.g., Cmd + , on macOS or Ctrl + , on Windows)

  • Go to Features → MCP or Tools & Integrations → MCP Tools

  • Make sure MCP servers are enabled

2: Create an Orq.ai API key

In Orq.ai, create an API key for the workspace or project you want Cursor to access.

Keep this key handy; you’ll reference it in mcp.json or in your shell environment.

3: Add the Orq MCP server to Cursor

Cursor gives you two main ways to add MCP servers: through the settings UI or by editing mcp.json directly.

Option A – Via Cursor settings (UI)

  1. Open Cursor → Settings → Cursor settings.

  2. Go to Tools & Integrations → MCP Servers (or Features → MCP).

  3. Click “Add New MCP Server”.

  4. Fill in the server details:

  • Name: orq

  • Transport: streamable-http or sse (depending on how Orq is exposed; use streamable-http if supported)

  • URL: https://my.orq.ai/v2/mcp

  1. If the UI supports environment variables or headers, configure your Orq API key as a bearer token (for example by referencing an env var like ORQ_API_KEY).

  2. Save and enable the server. A green indicator next to orq means the connection succeeded.

Option B – Edit mcp.json directly

Cursor stores MCP configuration in JSON files:

Global scope: ~/.cursor/mcp.json

Project scope: .cursor/mcp.json in your project root

Add an Orq entry, using a remote transport (for example, via an MCP proxy or direct HTTP client):

json

{

 "mcpServers": {

   "orq": {

     "url": "https://my.orq.ai/v2/mcp",

     "env": {

       "ORQ_API_KEY": "<your-orq-api-key>"

     }

   }

 }

}
json

{

 "mcpServers": {

   "orq": {

     "url": "https://my.orq.ai/v2/mcp",

     "env": {

       "ORQ_API_KEY": "<your-orq-api-key>"

     }

   }

 }

}
json

{

 "mcpServers": {

   "orq": {

     "url": "https://my.orq.ai/v2/mcp",

     "env": {

       "ORQ_API_KEY": "<your-orq-api-key>"

     }

   }

 }

}
json

{

 "mcpServers": {

   "orq": {

     "url": "https://my.orq.ai/v2/mcp",

     "env": {

       "ORQ_API_KEY": "<your-orq-api-key>"

     }

   }

 }

}

Save the file, then restart Cursor or click Reload in MCP settings so the AI assistant picks up the new server.

4: Start using Orq.ai tools from Cursor

Open the AI panel or Composer in Cursor and ask:

  • “What tools do you have access to?” or

  • “List Orq tools and query recent failed agent runs.”

Cursor will call Orq’s MCP tools if everything is configured correctly.


What Can You Do with Orq.ai + Cursor

Query observability data in natural language

Use Cursor’s AI to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your editor.

Design and run evaluations

Describe the behavior you want to test, let Cursor and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.

Compare prompts, models, and configs

From Cursor, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.

Generate reusable synthetic datasets

Ask Cursor’s AI to create challenging synthetic test cases for a workflow, such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.

Debug production regressions as a team

When something breaks, stay in Cursor. Pull recent traces for a deployment, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.

Query observability data in natural language

Use Cursor’s AI to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your editor.

Design and run evaluations

Describe the behavior you want to test, let Cursor and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.

Compare prompts, models, and configs

From Cursor, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.

Generate reusable synthetic datasets

Ask Cursor’s AI to create challenging synthetic test cases for a workflow, such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.

Debug production regressions as a team

When something breaks, stay in Cursor. Pull recent traces for a deployment, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.

Query observability data in natural language

Use Cursor’s AI to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your editor.

Design and run evaluations

Describe the behavior you want to test, let Cursor and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.

Compare prompts, models, and configs

From Cursor, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.

Generate reusable synthetic datasets

Ask Cursor’s AI to create challenging synthetic test cases for a workflow, such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.

Debug production regressions as a team

When something breaks, stay in Cursor. Pull recent traces for a deployment, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.

Query observability data in natural language

Use Cursor’s AI to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your editor.

Design and run evaluations

Describe the behavior you want to test, let Cursor and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.

Compare prompts, models, and configs

From Cursor, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.

Generate reusable synthetic datasets

Ask Cursor’s AI to create challenging synthetic test cases for a workflow, such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.

Debug production regressions as a team

When something breaks, stay in Cursor. Pull recent traces for a deployment, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.

Cursor direct vs with Orq.ai MCP

Capability

Cursor alone

Cursor + Orq.ai MCP

Query production LLM traces

No built‑in view into Orq.ai’s observability data.

Ask Cursor to list, filter, and group Orq.ai traces (failures, slow runs, agent tool calls, etc.) from inside the IDE.

Run experiments on prompts

Teams can iterate on prompts manually in chat, but no native experiment tracking in Orq.ai.

Create and run Orq.ai experiments comparing prompts, models, or configs against datasets, directly from your Cursor sessions.


Generate synthetic eval data

You can prompt Cursor to generate examples, then copy/paste them elsewhere.

Generate synthetic test cases and save them as reusable Orq.ai datasets for evals and experiments.

Pull cost and usage analytics

No view into Orq router or deployment analytics.

Query Orq.ai’s cost, usage, and performance metrics for models and deployments via MCP tools, then inspect them alongside your code in Cursor.

Run evaluators on datasets

No built‑in concept of Orq evaluators or datasets.

Work with Orq evaluators and datasets from Cursor, depending on the MCP tools enabled.


FAQs

Do I have to use Cursor to get value from Orq.ai?

No. Orq.ai works on its own through the UI and API. Cursor is an optional IDE front‑end for your workspace. You get the same experiments, evals, and observability in Orq; Cursor simply lets teams drive them from their editor using natural language.

What can Cursor see in my Orq.ai workspace, and how is access controlled?

Cursor only sees what the Orq API key you configure is allowed to access. If you use a project‑level key scoped to a specific workspace or environment, Cursor can only query traces, experiments, datasets, and deployments inside that scope. Rotate or revoke the key in Orq to instantly cut off access.

Can I point Cursor at different Orq environments (dev, staging, prod)?

Yes. You can create separate Orq entries in mcp.json or use different API keys per project, then enable the one you need per workspace. That way, you can run evals and inspect traces in dev or staging first, then switch the same Cursor setup to the production environment.

Does Cursor connect to Orq directly from my machine?

Yes. Cursor runs as a local IDE and connects to remote MCP servers over the network. Your Orq MCP endpoint must be reachable over the public internet (or via your network/VPN), and Orq handles authentication and scoping via your API key.

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Create an account and start building today.

Book a demo

Explore docs

Create an account and start building today.

Book a demo

Explore docs

Create an account and start building today.

Book a demo

Explore docs