About mcp-server-spira
mcp-server-spira connects MCP clients to Inflectra Spira for project, test, and requirements management. It supports SpiraTest, SpiraTeam, and SpiraPlan, with tools for searching, creating, updating, and recording test results.
Key features
- 01Search product- and program-level artifacts
- 02Retrieve artifact details and field schemas
- 03Create and update management artifacts
- 04Record automated test results
- 05Comment on and associate artifacts
- 06Query workspace and template metadata
Use cases
- 01Find assigned tasks and incidents
- 02Search requirements or test cases across products
- 03Create or update project management artifacts
- 04Write CI/CD test results to Spira
MCP support
As an MCP server, it exposes tools for MCP clients to access Spira, including artifact search and updates, test-result recording, and workspace and template queries.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx mcp-server-spiraINFLECTRA_SPIRA_BASE_URLThe base URL for your instance of Spira (typically https://mycompany.spiraservice.net or https://demo-xx.spiraservice.net/mycompany)INFLECTRA_SPIRA_USERNAMEThe login name you use to access SpiraINFLECTRA_SPIRA_API_KEYSecretThe API Key (RSS Token) you use to access the Spira REST APILocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Inflectra Spira Server
A Model Context Protocol (MCP) server that lets AI assistants interact with Inflectra Spira — covering project management, test management, and requirements management.
Works with SpiraTest, SpiraTeam, and SpiraPlan.
Table of Contents
Quick Start
Install from PyPI:
pip install mcp-server-spira
Add to your MCP client config:
{
"mcpServers": {
"inflectra-spira": {
"command": "python",
"args": ["-m", "mcp_server_spira"],
"env": {
"INFLECTRA_SPIRA_BASE_URL": "https://myinstance.spiraservice.net",
"INFLECTRA_SPIRA_USERNAME": "mylogin",
"INFLECTRA_SPIRA_API_KEY": "{XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXX}"
}
}
}
}
For Cline, add "type": "stdio" to the server config. For Claude Desktop:
mcp install src/mcp_server_spira/server.py --name "Inflectra Spira Server"
Configuration
Set these environment variables (or use a .env file):
| Variable | Required | Description |
|---|---|---|
INFLECTRA_SPIRA_BASE_URL | Yes | Base URL for your Spira instance |
INFLECTRA_SPIRA_USERNAME | Yes | Your Spira login name |
INFLECTRA_SPIRA_API_KEY | Yes | API Key (RSS Token) from your Spira user profile |
SPIRA_PROJECT_ID | No | Default product ID — avoids passing product_id on every call |
SPIRA_CREATE_TAGS | No | Comma-separated tag string merged into Tags on every artifact the MCP creates; empty or unset disables injection |
SPIRA_UPDATE_TAGS | No | Comma-separated tag string merged into Tags on every artifact the MCP updates; empty or unset disables injection |
Available Tools
13 tools organized by scope. All data-retrieval tools return structured JSON with field projection, filtering, and a consistent response envelope.
Search Tools
| Tool | Description |
|---|---|
mywork_search_artifacts | Your assigned items (task, incident, requirement, test_case, test_set) with filtering and field projection |
product_search_artifacts | Search artifacts in a product (11 types). Supports cross-product fan-out, server-side filtering, and nested sub-artifact includes. |
product_get_artifact | Single artifact by ID with full details and optional sub-artifact includes |
program_search_artifacts | Search program-level artifacts (capability, milestone) |
Write Tools
| Tool | Description |
|---|---|
product_create_artifact | Create artifacts (incident, task, requirement, test_case, risk, release, test_set, build, test_step, mitigation, requirement_step) |
product_update_artifact | Update existing artifacts |
product_record_test_run | Record automated test results from CI/CD |
create_comment | Add a comment to an artifact |
create_association | Link two artifacts (related-to, depends-on, coverage) |
Workspace & Configuration Tools
| Tool | Description |
|---|---|
workspace_search | List products, programs, or product templates with field projection |
workspace_get | Get a single product, program, or template by ID |
template_get_metadata | Types, statuses, priorities, severities, custom properties, and more for a template |
get_artifact_schema | Field schema for any artifact type (local-only, no API call) |
Usage Examples
Try these prompts with your AI assistant:
Show me my assigned tasks in Spira
List all products in my Spira instance
Get the open incidents in product 55
Search for critical requirements across products 55 and 60
Get the field schema for incidents
License
MIT — see LICENSE.
Built with MCP Python SDK · Spira REST API v7.0
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.