JIRA MCP Server

jira_get_create_fields

Retrieve required fields for a Jira project and issue type before creating an issue, including custom fields and allowed values, to avoid validation errors.

Instructions

IMPORTANT: Call this BEFORE creating an issue to discover required fields and their formats. Returns all required fields for a project and issue type, including custom fields, with allowed values and format hints. This prevents "field is required" errors during issue creation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "projectKey": "<projectKey>",
      "issueType": "<issueType>"
    },
    "name": "jira_get_create_fields"
  }
}

Input Schema

Name	Required	Description
`issueType`	Yes	The issue type name (e.g., "Task", "Bug", "Story")
`projectKey`	Yes	The project key (e.g., "PROJ")
`includeOptional`	No	If true, also return optional fields. Default: false (only required fields).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.13Jul 31, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It adds behavioral context: returns all required fields, includes custom fields, allowed values, and format hints. It does not disclose side effects, but this is a read-only operation. It could elaborate on response structure, but the essentials are present.

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 concise and front-loaded, beginning with an imperative directive and followed by a clear explanation of return contents. Every sentence earns its place, with no unnecessary detail.

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

Completeness4/5

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

Given no output schema and no annotations, the description gives a high-level overview of the returned data. It informs the agent of the key information needed to invoke the tool correctly. A bit more detail on response format would push it to 5, but it is adequate for the tool's purpose.

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

Parameters3/5

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

Schema description coverage is 100%, with each parameter clearly described. The description does not add extra parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 function: to discover required fields and formats before creating an issue. It distinguishes from siblings like jira_create_issue by focusing on pre-creation field discovery and from jira_get_field_schema by targeting required fields for a specific project and issue type.

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?

Provides explicit usage guidance: 'Call this BEFORE creating an issue' with rationale (prevents errors). Does not mention alternatives or when-not-to-use, so it lacks full exclusion criteria, but the context is clear and actionable.

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