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Inflectra Spira MCP Server

Inflectra Spira MCP Server

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by Inflectra

get_artifact_schema

Read-only

Returns the field schema for a Spira artifact type as JSON, listing each field's name, type, and description. Use it to discover available fields for tasks, incidents, requirements, and other artifacts.

Instructions

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

artifact_type: task, incident, requirement, test_case, release, risk, test_set, test_run, automation_host, capability, milestone Returns: {artifact_type, fields: [{name, type, description}]} or {error, valid_types}.

Input Schema

NameRequiredDescriptionDefault
artifact_typeYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.1.0
    • removedInput schema / properties / artifact_type / title
      Removed value: -"Artifact Type"
    • removedInput schema / title
      Removed value: -"get_artifact_schemaArguments"
  2. First observedv1.1.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare this as a safe read (readOnlyHint=true, destructiveHint=false, openWorldHint=false), and the description adds real behavioral detail: the exact return shape and the existence of an error branch that carries valid_types. That error-path disclosure is genuinely useful for recovery. It stops short of noting pagination or cost, but for a small read-only lookup that is a minor gap.

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

Conciseness4/5

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

Front-loaded purpose sentence, then the value list and return shape. Every element is compact. Slight redundancy in restating the return shape, which the output schema already defines, keeps it just under top marks.

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?

An output schema exists, so return values need no further explanation, and the description still supplies the accepted input values and error shape. Nothing an agent needs in order to call this correctly is missing.

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

Parameters4/5

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

Schema description coverage is 0% and the single parameter has no enum in the schema, so the description carries the full burden — and it does, enumerating all eleven accepted artifact_type values plus the error payload that returns valid_types. It adds substantial meaning beyond the bare 'type: string' 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?

States a specific verb and resource ('Returns the field schema for a Spira artifact type as JSON') and an explicit output format. It is clearly distinguishable from every sibling, which are search/get/create/update artifact tools rather than schema introspection.

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

Usage Guidelines3/5

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

The enumeration of valid artifact types implies the tool's role (discovering fields before creating or updating artifacts), but the description never states when to call it, when not to, or that it should precede product_create_artifact or product_update_artifact. Usage is inferable rather than spelled out.

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