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Inflectra

Inflectra Spira MCP Server

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by Inflectra

template_get_metadata

Read-only

Retrieve product template metadata sections including types, custom properties, statuses, priorities, severities, importances, probabilities, and impacts. Filter by artifact kind for targeted results.

Instructions

Retrieves template metadata sections for a product template.

    metadata_type (list[str], required): Sections to fetch.
      - "types": Artifact type definitions per artifact kind
        (e.g. Requirement types: Use Case, User Story).
      - "custom_properties": Custom field definitions per artifact kind.
      - "statuses": Status definitions per artifact kind
        (Requirement, Incident, Task, Risk, Release, Test Case, Document).
      - "priorities": Priority definitions per artifact kind
        (Incident, Task, Test Case).
      - "severities": Severity definitions (Incident only).
      - "importances": Importance definitions (Requirement only).
      - "probabilities": Probability definitions (Risk only).
      - "impacts": Impact definitions (Risk only).
    template_id (int, required): Numeric ID of the product template
      (e.g. 45 for PT:45).
    artifact_type (str, optional): Filter to a single artifact kind
      (e.g. "Requirement"). When omitted, all artifact kinds are fetched.

    Returns JSON with template_id, sections dict, and warnings list.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
template_idYes
metadata_typeNo
artifact_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already indicate a safe read operation (readOnlyHint=true, destructiveHint=false). The description adds transparency about the return structure (JSON with template_id, sections dict, warnings list) and that it fetches data for given types and optional artifact filter. No contradictions with annotations.

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?

The description is well-structured with clear sections for each parameter and a return note. It is detailed but not overly verbose, though the list of metadata_type values could be slightly trimmed. Front-loads the main purpose effectively.

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 presence of an output schema and the tool's moderate complexity (3 parameters, one required, enum values), the description covers all necessary aspects: parameter meanings, usage context, and return format. It is complete for an AI agent to select and invoke the tool correctly.

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?

The description provides detailed explanations for each metadata_type value, template_id, and artifact_type, significantly augmenting the schema which has 0% coverage. However, it inaccurately marks metadata_type as required, whereas the schema shows it's optional (default null). This minor error reduces precision.

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 it retrieves template metadata sections for a product template, specifying the exact metadata types available. It distinguishes itself from sibling tools like get_artifact_schema or search tools by focusing on template metadata.

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 implicitly guides usage by explaining each metadata_type option when to fetch specific sections. However, it does not explicitly state when not to use this tool or mention alternatives like get_artifact_schema for artifact definitions.

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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