When would you choose MCP over native Rovo connectors?

[AGASTYA SHARMA]

Hi Community,

I've been learning about both native Rovo connectors and the new MCP integration, and I'm trying to understand where each one fits best.

If both can give Rovo access to external information, when would you choose an MCP server instead of using a native connector?

Are there specific use cases where MCP provides more flexibility or better control, or are native connectors generally the preferred option whenever they're available?

I'd love to hear how others are making this decision in real projects.

Thanks in advance!

[Viswanathan Ramachandran]

Hi @AGASTYA SHARMA 

Thats thoughtful question. Here is my breakdown

Strategic Goal	Recommended Path	Why?
We want Atlassian Rovo to search our Google Drive, Slack, and Figma.	Native Connector	Fully managed, zero maintenance, handles indexing and permissions natively within Atlassian Cloud.
We want developers using Cursor/Claude to pull Jira specs while coding.	Atlassian Rovo MCP Server	Connects Atlassian context directly into external AI environments over secure OAuth 2.1.
We want an AI agent to query an internal, on-prem database.	Custom MCP Server	Easy to build, runs locally or within your private network, and conforms to open standards.
We need automated AI pipelines to trigger complex workflows across multiple tools.	MCP Integration	Standardised tool invocation designed specifically for multi-step agentic execution.

Most engineering leaders start with Native Connectors for broad organisation-wide knowledge retrieval (giving all employees search across common SaaS apps), while reserving MCP for deep technical workflows, developer tooling, and custom internal integrations where specific actions or non-standard data sources are required.

In short: Native Connectors pull external knowledge into Atlassian to make Rovo smarter. MCP projects Atlassian context and actions out into external AI clients and custom developer workflows.