mcp-server-rt
Connects AI assistants to a Request Tracker (RT) instance, enabling natural language ticket search, creation, updates, and queue management via the MCP protocol.
README
mcp-server-rt
An MCP (Model Context Protocol) server that connects AI assistants to a live RT (Request Tracker) instance. Search tickets, view history, create and update tickets — all from a natural language conversation.
Features
- Search tickets using RT's full TicketSQL query language
- Read ticket details including full transaction history
- Create tickets setting initial content and all ticket metadata: status, owner, requestors, due dates, custom fields, custom roles, and links
- Update tickets reply, comment, and update tickets, with the same full field support
- Queue and user discovery — list queues, inspect custom field definitions, look up users by name or email
- TicketSQL grammar reference — the AI can consult the full RT 6.0.2 syntax guide before constructing complex queries
Requirements
- RT 6.0 or later with REST 2.0 API enabled (included by default)
- Node.js 18 or later
- An RT authentication token
Installation
npm install -g mcp-server-rt
Or use without installing via npx mcp-server-rt.
Creating an RT Auth Token
In RT: Logged in as → Settings → Auth Tokens → Create
Give the token a name (e.g. "Claude") and copy the generated token string.
The token is associated with the user account, so all operations in RT from Claude using that token will be logged as performed by that user. So everything you do via Claude still gets logged in RT as you, including emails sent on comments and replies.
Users need to be granted the right ManageAuthTokens to see the Auth Tokens menu.
AI Client Setup
MCP is an open standard — this server works with any MCP-compatible AI client. Configuration varies by client.
Claude Desktop ✓ (tested)
Install the .mcpb extension package from the releases page. In the Claude app, go to Customize → Connectors, find RT, and enter your RT URL and auth token.
Alternatively, add manually to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"rt": {
"type": "stdio",
"command": "npx",
"args": ["mcp-server-rt"],
"env": {
"RT_URL": "https://rt.example.com",
"RT_TOKEN": "your-auth-token"
}
}
}
}
Claude Code ✓ (tested)
Add to .mcp.json in your project root:
{
"mcpServers": {
"rt": {
"type": "stdio",
"command": "npx",
"args": ["mcp-server-rt"],
"env": {
"RT_URL": "https://rt.example.com",
"RT_TOKEN": "your-auth-token"
}
}
}
}
Other MCP-compatible clients
Any client that supports MCP stdio servers should work. Consult your client's documentation for how to register a stdio MCP server with environment variables. The server entry point is mcp-server-rt (or node /path/to/dist/index.js for a local build).
Tools
| Tool | Description |
|---|---|
search_tickets |
Search tickets using RT's TicketSQL query language |
get_ticket |
Get details for a specific ticket by ID |
get_ticket_history |
Get transaction history (comments, replies, status changes) |
get_transaction |
Get full details of a single transaction, including decoded message content |
get_queue |
Get details about a queue by ID or name |
list_queues |
List all available queues |
get_queue_fields |
Get custom field definitions and lifecycle name for a queue |
lookup_user |
Search for RT users by name or email |
get_current_user |
Get the RT user associated with the configured auth token |
get_ticketsql_grammar |
Fetch the full TicketSQL grammar reference (for complex queries) |
create_ticket |
Create a new ticket |
update_ticket |
Update ticket fields (status, owner, priority, dates, watchers, links, custom fields) |
add_comment |
Add an internal comment (not visible to the requestor) |
add_reply |
Send a reply to the requestor |
get_ticket_attachments |
List all attachments on a ticket |
get_attachment |
Retrieve a single attachment by ID |
save_attachment |
Save an attachment to a local file |
Usage Examples
Example 1: Finding and triaging unowned tickets
User: "Show me active tickets in the Support queue with no owner."
Claude calls: search_tickets with query Queue = 'Support' AND Status = '__Active__' AND Owner = 'Nobody', requesting Subject, Status, Created, and Requestor fields.
Result: A table of unowned active tickets with subject, age, and requestor, ready to assign or act on.
Example 2: Reading recent correspondence on a ticket
User: "Show me the most recent reply on ticket 1234."
Claude calls: get_ticket_history to get the list of transactions, identifies the most recent Correspond entry, then calls get_transaction to fetch and decode the full message content.
Result: The decoded text of the reply, including who sent it and when.
Example 3: Creating a fully configured ticket
User: "Create a ticket in the Projects queue titled 'Update onboarding docs', assign it to alice, set the due date to next Friday, and link it to ticket 500."
Claude calls: create_ticket with Queue, Subject, Owner, Due, and RefersTo all set in a single API call.
Result: New ticket created with all fields set. Claude confirms the ticket number and a summary of what was set.
Example 4: Updating ticket status with a reply
User: "Resolve ticket 789 and let the requestor know we've pushed a fix in version 6.0.3."
Claude calls: add_reply with the message content and Status: 'resolved' to close the ticket and notify the requestor in one step.
Result: Ticket resolved, requestor notified. Claude confirms both actions completed.
Example 5: Querying with custom fields
User: "Find all open tickets in the General queue where the Category field is set to 'Bug'."
Claude calls: get_queue_fields to confirm the exact custom field name, then search_tickets with Queue = 'General' AND Status = '__Active__' AND CF.{Category} = 'Bug'.
Result: A list of matching bug tickets with subject, owner, and creation date.
How It Works
This server implements the Model Context Protocol over stdio. The AI client translates natural language requests into TicketSQL queries or RT API calls, invokes the appropriate tool, and presents the results. The server itself is a thin proxy — it passes queries directly to RT's REST 2.0 API and returns the JSON response.
For complex searches, the AI can call get_ticketsql_grammar to consult the full TicketSQL syntax reference before constructing a query.
Configuration Reference
| Environment Variable | Description |
|---|---|
RT_URL |
Base URL of your RT instance (e.g. https://rt.example.com) |
RT_TOKEN |
RT authentication token |
Development
npm install
npm run build # compile TypeScript to dist/
npm test # run tests
npm run dev # watch mode
Run locally against your RT instance:
RT_URL=https://rt.example.com RT_TOKEN=your-token node dist/index.js
Compatibility
- RT 6.0+ (REST 2.0 API)
- Node.js 18+
Privacy
This server does not collect, store, or transmit any data to Best Practical or any third party. All communication is directly between your AI client and your own RT instance using the URL and credentials you provide. No usage data, ticket content, or credentials are sent anywhere other than your configured RT server.
See the Best Practical Privacy Policy for general information about our privacy practices.
Support
For questions and discussion, visit the Best Practical Community Forum.
To report a bug, create a ticket on our public RT instance. Note that this is a public RT instance, so the information you share will be visible to others.
Is RT mission critical for you? Commercial support for RT and this connector is available from Best Practical. Contact us at sales@bestpractical.com.
License
GPL-2.0
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.