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jira_batch_get_changelogs

Retrieve change history for multiple Jira issues in one batch call. Optionally filter by fields and control results per issue. Jira Cloud only.

Instructions

Get changelogs for multiple Jira issues (Cloud only).

Args: ctx: The FastMCP context. issue_ids_or_keys: List of issue IDs or keys. fields: List of fields to filter changelogs by. None for all fields. limit: Maximum changelogs per issue (-1 for all).

Returns: JSON string representing a list of issues with their changelogs.

Raises: NotImplementedError: If run on Jira Server/Data Center. ValueError: If Jira client is unavailable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of changelogs to return in result for each issue. Default to -1 for all changelogs. Notice that it only limits the results in the response, the function will still fetch all the data.
fieldsNo(Optional) Filter the changelogs by fields, e.g. ['status', 'assignee']. Default to None for all fields.
issue_ids_or_keysYesList of Jira issue IDs or keys, e.g. ['PROJ-123', 'PROJ-124']

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must carry the burden. It discloses the return type (JSON string), Cloud-only limitation, and raises (NotImplementedError, ValueError). However, it omits the nuance that 'limit' only limits the response and the function still fetches all data, which is a notable behavioral detail.

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 Args, Returns, and Raises sections. It is concise and front-loaded with the core purpose and scope, with no redundant or filler content.

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?

The description covers the tool's purpose, key restriction (Cloud only), return format, and error conditions. With an output schema present, it doesn't need to detail every return field. The missing fetch-all-data nuance is already captured in the input schema, so the overall context is sufficiently complete.

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 coverage is 100%, and the description largely repeats parameter info from the schema. It adds minimal value beyond what the schema already states, such as 'None for all fields'. The schema provides the more detailed notice about limit behavior.

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 uses a specific verb and resource: 'Get changelogs for multiple Jira issues', with an explicit scope 'Cloud only'. This clearly distinguishes it from sibling tools like jira_get_issue or jira_get_transitions.

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 states the tool is for multiple issues and includes a 'Cloud only' restriction, which is an important exclusion (not supported on Server/Data Center). However, it does not explicitly name alternatives or provide when-not-to-use guidance beyond the Cloud-only constraint.

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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