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jira_batch_get_changelogs

Retrieve changelogs for multiple Jira Cloud issues. Optionally filter by specific fields and set a maximum number per issue.

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
Behavior5/5

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

With no annotations provided, the description fully conveys behavioral traits: Cloud-only restriction, raises NotImplementedError and ValueError, and notes that the limit param only truncates response but still fetches all data. This provides comprehensive transparency.

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 Args, Returns, and Raises sections. It is concise but includes necessary details. The first line provides immediate clarity, and the structure aids comprehension.

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?

The description covers all aspects: purpose, parameters, return type (JSON string), error cases, and Cloud-only constraint. With 100% schema coverage and a clear returns statement, no gaps remain.

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%, so baseline is 3. The parameter descriptions in the Args section are nearly identical to the schema descriptions, adding no new semantics. The description does not compensate beyond what the schema already provides.

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 'Get changelogs for multiple Jira issues (Cloud only).' This provides a specific verb+resource combination and distinguishes it from other tools that operate on single issues or search.

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 indicates when to use this tool (when changelogs for multiple issues are needed) through its name and first sentence. It does not explicitly contrast with alternatives, but no sibling tool serves the same purpose, making usage clear.

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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