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update_issue

Change a GitLab issue's title, description, labels, assignees, milestone, or state to close or reopen it.

Instructions

Update issue title, description, state (close/reopen), milestone, labels, or assignees

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoUpdated issue title
labelsNoSet labels (replaces all existing labels)
due_dateNoDue date in YYYY-MM-DD format
issue_iidYesThe project-scoped issue number (e.g. 14 for #14)
add_labelsNoAdd labels without removing existing ones
project_idYesProject numeric ID (e.g. 31) or namespace path (e.g. 'group/project-name'). URL-encoding is handled automatically.
descriptionNoUpdated issue description markdown
state_eventNoChange issue status to closed or reopened
assignee_idsNoArray of user IDs to assign (replaces existing assignees)
milestone_idNoMilestone ID to assign (pass 0 to unassign)
remove_labelsNoRemove specific labels from the issue
milestone_titleNoMilestone title to assign (auto-resolved to ID)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It hints that state means close/reopen, but ignores the destructive semantics that the schema encodes – that labels replaces all existing labels, that assignee_ids replaces existing assignees, and how milestone_id=0 unassigns. For a bare mutation tool these are meaningful gaps.

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?

A single front-loaded sentence that enumerates the capabilities with no filler. It is efficient, though the field list would carry more value if it distinguished replace-vs-add behavior rather than just naming fields.

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

Completeness3/5

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

For a 12-parameter mutation tool with no annotations and no output schema, the description covers most mutable fields but omits due_date and the add/remove/replace label distinction, and says nothing about permissions, side effects, or reversibility. Adequate but with clear gaps.

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 parameters are already fully documented in the schema and the baseline is 3. The description lists fields but adds no syntax, format, or replace-vs-append meaning beyond what the schema states.

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?

States a specific verb (Update) and resource (issue) and enumerates the mutable fields, so an agent can distinguish it from create_issue, get_issue and list_issues without opening a schema. It does not explicitly name or contrast a sibling, which keeps it just short of a 5.

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?

There is no when-to-use or when-not-to-use guidance and no routing to alternatives, even though the schema exposes overlapping operations (labels vs add_labels vs remove_labels, milestone_id vs milestone_title) that clearly warrant disambiguation. The reader is left to infer usage entirely.

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