Jira MCP Server by CData – FAQ | MCP Marketplace

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Frequently Asked Questions (FAQ)

Q: What is an MCP Server?

A: MCP (Model Context Protocol) Server acts as a bridge, allowing AI models to access and interact with external data sources and tools.

Q: What is the purpose of the Jira MCP Server?

A: It allows LLMs (like Claude Desktop) to query live Jira data supported by the CData JDBC Driver for Jira using natural language questions.

Q: Is this MCP Server read-only?

A: Yes, this specific server is read-only. A free (beta) read/write server is available at https://www.cdata.com/solutions/mcp

Q: What is a .prp file and what information does it contain?

A: A .prp file is a configuration file that contains properties for your JDBC connection, such as the driver path, class name, JDBC URL, server name, and prefix.

Q: What CData JDBC Drivers supported by MCP Server?

A: The Jira MCP Server uses the CData JDBC Driver for Jira.

Q: How do I configure Claude Desktop to use the MCP Server?

A: You need to modify the claude_desktop_config.json file to add the new MCP Server, specifying the command and arguments for running the server.

Q: What if I cannot see my CData MCP Server in Claude Desktop?

A: Ensure you have fully quit Claude Desktop (using Task Manager on Windows or Activity Monitor on Mac) and restarted it.

Q: What if Claude Desktop is unable to retrieve data?

A: Verify that you have configured your connection properly using the Connection String builder and that the connection string is correctly entered in the .prp file.

Q: Where can I get help if I have trouble connecting to my data source?

A: Contact the CData Support Team at https://www.cdata.com/support/submit.aspx

Q: What license is this MCP server under?

A: This MCP server is licensed under the MIT License.

Q: What is UBOS?

A: UBOS is a Full-stack AI Agent Development Platform focused on bringing AI Agents to every business department. Our platform help you orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents with your LLM model and Multi-Agent Systems.

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