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Inflectra

Inflectra Spira MCP Server

Official
by Inflectra
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Python
Remote

get_artifact_schema

Read-only

Retrieve the field schema for any Spira artifact type, returning field names, types, and descriptions.

Instructions

Returns the field schema for a Spira artifact type as JSON.

    Args:
        artifact_type: One of: task, incident, requirement, test_case,
            release, risk, test_set, test_run, automation_host,
            capability, milestone

    Returns:
        JSON: {"artifact_type": "...", "fields": [{"name", "type",
            "description"}, ...]}
        or {"error": "...", "valid_types": [...]} for unknown types.

    Call get_artifact_schema(artifact_type='task') to see fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
artifact_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.6/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true and destructiveHint=false, and the description adds value by specifying the return format (success/error) and behavior for unknown types. No contradictions.

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 with clear sections (purpose, args, returns, example). Every sentence adds value; no wasted words.

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 single parameter, presence of output schema, and annotations, the description is complete. It covers purpose, parameters, return format, error handling, and usage example.

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 compensates fully by listing all valid artifact types and providing an example call. This adds critical meaning beyond the bare schema.

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 the tool returns the field schema for a Spira artifact type as JSON, listing all valid artifact types. It distinguishes itself from sibling tools (e.g., search, create) by focusing on schema retrieval.

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?

The description provides an example call and lists valid artifact types, giving clear usage context. However, it does not explicitly state when to use this tool over siblings or exclude cases like non-existent types (though error return is specified).

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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