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How to Integrate Agents into Cursor

Jessica Shen

AI-assisted development is evolving fast, and Cursor is leading this charge. Marketed as an AI-native code editor, Cursor gives developers a way to collaborate with large language models (LLMs) directly in their IDE. Unlike ChatGPT or other chat-based interfaces, Cursor is built for engineers: it understands your repository, refactors your code, and can reason across multiple files in real time.

But, the power of Cursor isn’t limited to its built-in intelligence. By integrating third-party agents, developers can connect specialized models directly into their workflow, fully customizing and optimizing their development environment.

One of the most promising integrations is with Credal, an agent platform that brings enterprise-grade governance and data access controls to AI. In this article, we’ll explore how to integrate third-party agents, like Credal, into Cursor, and turn your IDE into an enterprise-ready workspace.

What is Cursor?

Cursor is an AI-enhanced code editor that brings large language models (LLMs) directly into your development workflow. Built on the familiar foundation of Visual Studio Code, it adds features like intelligent code generation, in-editor explanations, and multi-file refactoring by understanding your entire codebase.

What sets Cursor apart is its support for agent-driven workflows. Instead of merely completing lines of code, it can reason across files, run commands, and invoke external tools via the Model Context Protocol (MCP).

Developers can configure Cursor to use different model providers, define custom agent behaviours, and bring external services into the editor. The result is a collaborative assistant that goes beyond autocomplete, capable of helping with architecture-level changes and multi-step tasks.

Why integrate 3rd party agents?

Integrating third-party agents into your development toolkit allows you to extend Cursor beyond its built-in model and features by connecting in specialized models, tools, or data sources. Different agents have different strengths — some excel at reasoning, others at retrieval, and still others at secure enterprise workflows. By picking the right agent, you can tailor your workflow to performance, cost, or compliance needs.

Agent vs. Model: In this context, an “agent” refers to a system that can evaluate a goal, select or invoke tools, perform tasks in multiple steps and adapt based on outcomes, not simply a model that generates text.

What is MCP and why do we need it?

To enable third-party agent integration, Cursor uses the Model Context Protocol (MCP) — an open standard for securely linking AI agents with external tools, services and data sources. MCP defines how an agent calls external functions, retrieves context (i.e., relevant data), and exchanges structured responses in a consistent, auditable way.

By using MCP, developers can extend Cursor’s capabilities with APIs, databases or internal services, without needing to embed credentials inside the model or expose sensitive logic. In other words, MCP acts as the bridge that lets third-party agents integrate seamlessly into Cursor’s agentic environment.

How to integrate 3rd party agents in Cursor

By connecting an MCP-compliant tool server, you give Cursor’s reasoning engine access to new functions and data flows.

In the tutorial that follows, we’ll walk through how to integrate Credal (a secure enterprise AI-agent platform) into Cursor using MCP. This assumes basic Python proficiency.

Step 1: Enable API Access for your Credal agent

Open the agent in Credal → Deploy → enable Deploy Agent over API. Note the Agent ID, and copy the API key. Credal’s endpoint to send a message requires agentId, message, userEmail and Bearer auth; conversationId is optional.

Step 2: Create an MCP server that calls Credal

Install libraries

First, install the necessary libraries by running in your terminal:pip install "mcp[cli]" httpx

This installs the Official MCP Python SDK. We'll need this to define the MCP server below.

Set up environment variables

Run the following in your terminal, using the keys you generated in Step 1:

export CREDAL_API_KEY="YOUR_CREDAL_KEY" export CREDAL_AGENT_ID="YOUR_AGENT_UUID"

Define the MCP server

Now, we can define the server. Let's call this file credal_mcp.py.

import os import httpx from mcp.server.fastmcp import FastMCP # Environment variables (set these before running): # CREDAL_API_KEY - from Credal Deploy tab # CREDAL_AGENT_ID - UUID of your Credal agent CREDAL_API_KEY = os.environ["CREDAL_API_KEY"] CREDAL_AGENT_ID = os.environ["CREDAL_AGENT_ID"] mcp = FastMCP("Credal MCP") @mcp.tool(name="credal.send_message") def send_message(message: str, user_email: str, conversation_id: str | None = None) -> dict: """ Send a message to a Credal agent and return the JSON response. """ url = "<https://api.credal.ai/api/v0/copilots/sendMessage>" headers = {"Authorization": f"Bearer {CREDAL_API_KEY}", "Content-Type": "application/json"} payload = {"agentId": CREDAL_AGENT_ID, "message": message, "userEmail": user_email} if conversation_id: payload["conversationId"] = conversation_id with httpx.Client(timeout=60) as client: r = client.post(url, headers=headers, json=payload) r.raise_for_status() return r.json() if __name__ == "__main__": # Exposes an MCP server over stdio mcp.run()

This python file will be how Cursor is able to communicate with the Credal agent.

Step 4: Register the MCP server in Cursor

In this step, you connect your local MCP server to Cursor so your Credal agent appears as an available tool for its built-in Composer agent. The Composer agent is Cursor’s main reasoning and execution system: it reads context, decides which tools to invoke, and runs them on your behalf.

Open Cursor → Settings → Features → MCP → + Add New MCP Server.

Type: stdio
Command: python credal_mcp.pyOnce saved, Cursor automatically detects the Credal tool (credal.send_message) and allows the Composer agent to call it when relevant. This step effectively gives Cursor secure, structured access to your Credal agent through the MCP layer.

Step 5 [Optional]: Conversation control

You may pre-create a conversation (or let sendMessage create one) and pass conversationId to continue context. This step isn't necessary, but if will allow your agent to use your past queries to better answer future ones.

Why choose Credal?

Credal is a secure, enterprise-grade agentic platform designed for organizations that require fine-grained control over data, permissions, and compliance. It enables developers to deploy and govern AI agents that operate safely within existing enterprise systems while maintaining auditability and transparency.

Credal is an enterprise-grade platform built to deploy agents. It is designed for organizations that need fine-grained control over data, permissions, and compliance.

Credal offers several key features:

Permission Mirroring – The platform synchronizes access rights from source systems (e.g., Slack, Google Drive, Salesforce) so agents only see what the user already can see, preventing privilege escalation.
Secure Data Access – Credal includes native connectors for enterprise-SaaS and internal data stores, making it simple to plug into Box, Atlassian, Google Drive and more, with full permissions enforcement.
Audit and Observability – All agent interactions are logged centrally, enabling traceability, monitoring and compliance reviews.
Memory Portability – Agents can share context through a unified memory store (so previous work is carried forward), supporting multi-step workflows rather than one-off chats.
Registry and Discoverability – Credal maintains a catalog of agents and tools within the organization, making it easier to reuse, share, and manage them centrally.

For developers, integrating Credal with Cursor means you get agents that align with your org’s security posture, use existing data infrastructure, and provide accountable audit trails. This setup allows teams to prototype, deploy, and supervise sophisticated AI-powered workflows without sacrificing governance or trust.

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