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nulab

Backlog MCP Server

get_issues

Find and retrieve Backlog issues using filters for project, status, assignee, dates, and custom fields to get exactly the list you need.

Instructions

Returns list of issues

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort field
countNoNumber of issues to retrieve
orderNoSort order
offsetNoOffset for pagination
keywordNoKeyword to search for in issues
statusIdNoStatus IDs
projectIdNoProject IDs
versionIdNoVersion IDs
assigneeIdNoAssignee user IDs
categoryIdNoCategory IDs
priorityIdNoPriority IDs
issueTypeIdNoIssue type IDs
milestoneIdNoMilestone IDs
createdSinceNoCreated since (yyyy-MM-dd)
createdUntilNoCreated until (yyyy-MM-dd)
customFieldsNoCustom field filters (text, numeric, date, or list)
dueDateSinceNoDue date since (yyyy-MM-dd)
dueDateUntilNoDue date until (yyyy-MM-dd)
resolutionIdNoResolution IDs
updatedSinceNoUpdated since (yyyy-MM-dd)
updatedUntilNoUpdated until (yyyy-MM-dd)
createdUserIdNoCreated user IDs
parentIssueIdNoParent issue IDs
startDateSinceNoStart date since (yyyy-MM-dd)
startDateUntilNoStart date until (yyyy-MM-dd)
Behavior2/5

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states 'Returns list of issues' without mentioning pagination, default sorting, filter behavior, or return format. This is insufficient for a tool that clearly supports extensive filtering and pagination via its schema.

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 extremely concise—a single sentence with no fluff. However, it is under-specifying for a tool with this many parameters and no other documentation. It earns its place but offers minimal value, so it's not exceptional.

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

Completeness2/5

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

The tool is highly complex with 25 parameters, no output schema, and no annotations. The description provides only a minimal hint about return value ('list of issues') and says nothing about default behavior, filtering, or pagination. This is incomplete for practical use.

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 every parameter having a descriptive comment. The description itself adds no information beyond the schema, so the baseline score of 3 is justified. It neither helps nor hurts parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Returns list of issues' clearly identifies the verb (returns) and resource (list of issues), but it doesn't distinguish this from sibling tools like get_issue (singular) or count_issues. It's clear but lacks sibling differentiation, so a score of 4 is appropriate.

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?

The description provides no guidance on when to use this tool versus alternatives like get_issue, get_related_issues, or count_issues. There is no mention of filtering, pagination, or any preconditions. This is a clear gap for a tool with 25 parameters.

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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