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Inflectra

Inflectra Spira MCP Server

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by Inflectra

product_create_automated_test_run

Record automated test results in Spira from CI/CD pipelines, including test name, status, and error count, to track quality.

Instructions

    Records an automated test result in Spira.

    Maps to Spira API: POST /projects/{product_id}/test-runs/record

    Use this to push automated test results from CI/CD pipelines into Spira for quality tracking.

    Args:
        product_id: The numeric ID of the product (e.g., 55 for PR:55). If omitted, uses SPIRA_PROJECT_ID from environment.
        test_name: The name of the test being run
        short_message: Brief result description (50 chars or less)
        long_message: Full test outcome description in plain text
        error_count: Number of errors during test (0 if none)
        test_case_id: Test case ID without TC prefix (e.g., 12 for TC:12)
        execution_status_id: Status (1=Failed, 2=Passed, 3=Not Run, 4=N/A, 5=Blocked, 6=Caution)

    Returns:
        JSON: {"test_run_id": "TR:123", "message": "Test run recorded successfully"}

    Error Responses:
        Returns structured JSON with error, error_code, details, and suggestion.
        Common error codes: INVALID_PARAMETER, API_ERROR, NOT_FOUND

    Example Usage:
        result = record_automated_test_run(
            product_id=55, test_name="test_login", short_message="Passed",
            long_message="Login successful", error_count=0, test_case_id=123, execution_status_id=2
        )
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
test_nameYes
short_messageYes
long_messageYes
error_countYes
test_case_idYes
execution_status_idYes
product_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description details the REST method (POST), return format, error responses with common codes, and even includes an example. It goes well beyond the minimal annotations, offering rich behavioral insight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured into purpose, API mapping, usage, args, return, errors, and example. Every section serves a purpose, and it is not overly verbose for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 7 parameters and 6 required, the description covers return values (sample JSON), error responses, and example usage. It is complete for an agent to understand and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining each parameter: product_id's default from environment, short_message's 50-char limit, test_case_id without prefix, execution_status_id with status mapping. It adds essential meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Records an automated test result in Spira' and maps to the specific API endpoint. It distinguishes from sibling tools like product_create_build by focusing on test run recording.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear usage context: 'Use this to push automated test results from CI/CD pipelines.' While it doesn't explicitly exclude alternatives, the purpose is well-defined among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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