Introducing Open MCP Chat in Inflectra
With MCP Chat, you no longer need to know which report to run, which screen to navigate to, or even exactly where the information you need is stored. Instead, you can simply ask Inflectra.ai a question in natural language.
For example:
“What are the main blockers affecting this project?”
“Do I have any incidents that look like duplicates?”
“Which test cases should we prioritize for automation?”
“What are the biggest risks to the next release?”
“Which requirements have the weakest test coverage?”
Inflectra.ai can use the context available in Spira to investigate the question and provide an answer based on your actual project information.
This is an important step toward turning Spira from a system that simply stores lifecycle information into a platform that can actively help teams understand it.
From AI Features to an AI Conversation
Inflectra.ai already provides a growing set of specialized AI capabilities inside Spira.
Users can generate artifacts such as requirements, test cases, tasks, and risks, and use purpose-built AI analyses to examine areas such as requirements coverage and automation readiness. These capabilities are valuable because they apply AI to clearly defined software delivery activities.
More recently, we introduced the Helper AI Agent, giving users a conversational way to ask questions about how to use Spira. The Helper Agent searches Spira documentation, remembers the context of the conversation, and supports multiple conversation threads so users can ask follow-up questions without leaving their current workflow.
MCP Chat adds another dimension.
Instead of asking:
“How do I view the risks associated with a release?”
you can now ask:
“What are the most important risks affecting this release?”
The first is a question about how to use Spira. The second requires Spira to help you understand your project. That distinction is at the heart of MCP Chat.
Ask Questions About Your Actual Project
Software development teams accumulate a huge amount of information during the lifecycle of a project:
- Requirements describe what needs to be built.
- Tasks show the work being performed. Incidents capture defects and issues.
- Test cases document how functionality is verified.
- Releases organize delivery.
- Risks identify potential problems
- and relationships between these artifacts provide the traceability that connects everything together.
Traditionally, answering an important question often means navigating through several different views, filtering lists, examining dashboards, or creating reports. The new MCP Chat provides another way to interact with that information.
Instead of starting with the question “Where in Spira can I find this?”, users can start with the question they actually want answered.
For a project manager, that might be:
“What is currently blocking this project?”
For a QA manager:
“Which high-priority test cases would give us the greatest value if we automated them?”
For a product owner:
“Are there requirements with a large number of unresolved incidents?”
For a development lead:
“Do any of our open incidents appear to describe the same underlying problem?”
And for an executive or program manager:
“Give me a summary of the issues that could put the next release at risk.”
The goal is not simply to make Spira easier to search. It is to make the lifecycle information stored in Spira easier to understand.
Powered by the Spira MCP Server
Behind MCP Chat is Inflectra's next-generation Spira Model Context Protocol Server. Earlier this year, we expanded the Spira MCP Server from a read-only interface into a bidirectional integration platform. AI assistants can now retrieve information from Spira as well as create and update artifacts, add comments, perform advanced searches, work with custom properties, and retrieve relationships between artifacts. The MCP Chat brings that same technology directly into the Spira user experience.
When a user asks a question, the AI agent can determine which information it needs, use the available MCP capabilities to retrieve the relevant project context, and then reason across that information to formulate an answer. The user does not need to understand MCP, construct an API query, or know exactly which Spira artifacts need to be examined, they simply ask the question and Spira provides the answer.
Moving Beyond Simple Search
This is also why MCP Chat is fundamentally different from a conventional search box. A search engine is excellent when you know what you are looking for. You provide a keyword or filter and it returns matching records.
Many of the most valuable questions in software delivery are not like that.
Consider:
“What are the blockers for this project?”
There may not be an artifact explicitly called a “blocker.” Answering the question may require looking at overdue tasks, unresolved high-priority incidents, dependencies, risks, release dates, and other project information.
Similarly:
“Do we have any duplicate incidents?”
There may be no exact textual match between two defects. The descriptions could use different terminology while referring to essentially the same problem.
Or:
“Which tests should we automate first?”
There is rarely a single database field containing the answer. Making that assessment may require considering factors such as priority, execution frequency, test history, associated requirements, and other available context. These are questions that require reasoning across project information, rather than simply retrieving a single record. That is where conversational AI becomes especially interesting.
Project Intelligence in Natural Language
We believe MCP Chat represents the beginning of a new way of interacting with application lifecycle management platforms.
For years, organizations have invested enormous effort in collecting structured lifecycle information. Spira brings requirements, planning, development, testing, defects, releases, risks, and traceability together in one integrated system. The next challenge is making that information easier to turn into decisions. Instead of building another dashboard for every possible question, AI gives users the ability to interrogate the underlying lifecycle information dynamically.
For example, you might start a conversation by asking:
“What are the biggest problems affecting this release?”
Then follow up with:
“Which of those are related to our highest-priority requirements?”
And then:
“Show me the testing associated with those requirements.”
Because the interaction is conversational, users can progressively investigate a problem rather than needing to formulate the perfect query at the beginning.
The result is something closer to having an intelligent project analyst available inside Spira.
Three Ways to Use Inflectra.ai in Spira
With the addition of MCP Chat, Inflectra.ai now supports several complementary ways of working.
- First, there are prebuilt AI operations designed for specific activities such as generating artifacts or performing defined types of analysis. These are ideal when the task is well understood and a repeatable AI workflow provides the best experience. Also, for operations where you want consistency of results (e.g. generate the audit records for this artifact), pre-built operations ensure different users get the same results.
- Second, there is the Helper AI Agent, designed to answer questions about Spira itself. Users can ask how features work, clarify terminology, and get assistance while remaining inside the application.
- And now there is MCP Chat, where the conversation is open-ended and grounded in the actual information stored in the user's Spira environment.
Together, these approaches let users choose the level of AI assistance appropriate to the task rather than forcing every interaction through a single interface.
An Important Step Toward the Agentic SDLC
MCP Chat also fits into our broader vision for the future of software development:
- AI is rapidly moving beyond generating text and code. Increasingly, AI agents can reason about objectives, use tools, retrieve information from multiple sources, and perform multi-step workflows.
- The Model Context Protocol provides an important foundation for this world because it gives AI agents a standardized way to interact with systems such as Spira.
- Our next-generation MCP Server already enables external AI development environments to work directly with Spira rather than merely reading its information.
- MCP Chat brings that agentic architecture directly into Spira itself.
Today, that means being able to ask deeper questions about your projects and receive useful answers. Over time, the same architecture opens the door to increasingly sophisticated workflows in which AI does not merely tell you what is happening, but can help you decide what should happen next.
Available Now
The new Inflectra.ai MCP Chat is available now in Spira, alongside the existing Helper AI Agent and Inflectra.ai's growing collection of specialized artifact-generation and analysis capabilities.
For users, the experience is deliberately simple.
Open the chat and ask Spira what you want to know.
“What is blocking our release?”
“Which incidents might be duplicates?”
“Where should we focus our testing?”
“What should we automate next?”
Underneath those simple questions is something much more significant: an AI agent that can draw upon the rich lifecycle context already stored inside Spira.
For years, Spira has helped organizations capture the information they need to manage complex software projects.
Now, you can start having a conversation with it.
