Inflectra Spira MCP Server

Overview

The MCP Inflectra Spira Server bridges the gap between conversational AI assistants and the rich feature set of the Inflectra Spira suite. By exposing Spira’s REST API through the Model Context Protocol, it allows assistants such as Claude to query, create, and update project artifacts directly from natural language commands. This eliminates the need for developers to manually switch between a chat interface and Spira’s web UI, streamlining workflows that involve requirements tracking, test management, and program oversight.

At its core, the server implements a comprehensive set of tools that mirror Spira’s three editions—SpiraTest, SpiraTeam, and SpiraPlan. Each edition’s domain objects (tasks, requirements, incidents, test cases, etc.) are represented as discrete MCP tools, enabling fine‑grained operations. For example, the My Work suite lets an assistant retrieve all artifacts assigned to a user, while the Workspaces tools expose program and product manipulation capabilities. This design ensures that developers can keep context across multiple projects or programs without leaving the AI environment.

Key capabilities include:

• Artifact Retrieval and Manipulation: Read, create, update, and delete tasks, requirements, incidents, test cases, and more across both program and product scopes.
• Workspace Management: List, modify, and organize programs, products, and templates to keep project structures aligned with organizational standards.
• Automation Integration: Record automated test runs and CI/CD build results directly into Spira, providing a single source of truth for quality metrics.
• Specification Access: Pull product specification files that can be consumed by agentic AI tools, enabling code generation or feature modeling from formal specs.

Real‑world use cases abound. A QA engineer can ask the assistant to “list all pending test runs for Product X” and receive a ready‑to‑use JSON payload. A product owner can request “create a new requirement titled ‘Login Flow’ under the current sprint” and have it added without opening Spira. In continuous delivery pipelines, a CI tool can trigger the Builds tool to log success or failure directly into Spira, keeping stakeholders informed in real time.

Integrating this server into an AI workflow is straightforward: the assistant loads the MCP tools, authenticates with Spira credentials, and begins issuing high‑level commands. The server translates these into REST calls, handles pagination and error mapping, and returns structured responses that the assistant can present or further process. This tight coupling between natural language understanding and Spira’s domain logic gives developers a powerful, low‑friction way to automate routine tasks, enforce consistency, and accelerate delivery across the entire product lifecycle.