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nulab

Backlog MCP Server

get_pull_request_comments

Retrieve pull request comments from Backlog to review discussions, with optional filters for count, ID range, and sort order.

Instructions

Returns list of comments for a pull request

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of comments to retrieve
maxIdNoMaximum comment ID
minIdNoMinimum comment ID
orderNoSort order
numberYesPull request number
repoIdNoRepository ID
repoNameNoRepository ID
projectIdNoThe numeric ID of the project (e.g., 12345)
projectKeyNoThe key of the project (e.g., 'PROJECT')
Behavior2/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It merely restates the function without mentioning pagination, filtering, sorting, read-only nature, or any other behavioral traits. Key parameters like count, order, minId, and maxId are not discussed, so the agent is unaware of how results are constrained.

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

Conciseness3/5

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

The description is succinct at one sentence, but it under-specifies the tool's behavior. It is not needlessly verbose, yet it borders on a restatement of the tool name, lacking informative structure for a tool with nine parameters.

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

Completeness1/5

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

Given the tool has nine parameters and no output schema, the description is seriously incomplete. It does not explain how to identify the pull request (e.g., number vs repoId/repoName), how filtering parameters affect results, or what the response structure looks like, leaving the agent without necessary context for correct invocation.

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%, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides for each parameter, and it does not explain how parameters interact or which are needed beyond the required 'number'.

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 tool's purpose with a specific verb ('Returns list') and resource ('comments for a pull request'). This distinguishes it from siblings like get_issue_comments and get_pull_request, and the scope is immediately obvious.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives such as get_issue_comments or get_pull_request. There are no explicit context cues, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.

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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