A Model Context Protocol (MCP) server enabling AI assistants to interact with Spira by Inflectra.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"inflectra-spira": {
"env": {
"INFLECTRA_SPIRA_API_KEY": "{XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXX}",
"INFLECTRA_SPIRA_BASE_URL": "https://myinstance.spiraservice.net",
"INFLECTRA_SPIRA_USERNAME": "mylogin"
},
"args": [
"-m",
"mcp_server_spira"
],
"command": "python"
}
}
}Are you the author?
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A Model Context Protocol (MCP) server enabling AI assistants to interact with Spira by Inflectra.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'mcp-server-spira' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked mcp-server-spira against OSV.dev.
Click any tool to inspect its schema.
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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.
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"
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 |
10 tools organized by scope. All data-retrieval tools return structured JSON with field projection, filtering, and a consistent response envelope.
| 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) |
| Tool | Description |
|---|---|
product_create_automated_test_run | Record automated test results from CI/CD |
product_create_build | Record a CI/CD build result |
| 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) |
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
MIT — see LICENSE.
Built with MCP Python SDK · Spira REST API v7.0