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gitlab_summarize_issue

Generate AI-friendly summaries of GitLab issues by condensing titles, descriptions, comments, and status into concise formats suitable for LLM processing.

Instructions

Generate AI-friendly issue summary Returns: Condensed issue information Use when: Processing issues with AI Includes: Title, description, comments, status

Smart truncation:

  • Preserves key information

  • Removes redundancy

  • Fits context limits

Related tools:

  • gitlab_get_issue: Full details

  • gitlab_summarize_pipeline: Pipeline summaries

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoProject identifier (auto-detected if not provided) Type: integer OR string Format: numeric ID or 'namespace/project' Optional: Yes - auto-detects from current git repository Examples: - 12345 (numeric ID) - 'gitlab-org/gitlab' (namespace/project path) - 'my-group/my-subgroup/my-project' (nested groups) Note: If in a git repo with GitLab remote, this can be omitted
issue_iidYesIssue number (IID - Internal ID) Type: integer Format: Project-specific issue number (without #) Required: Yes Examples: - 123 (for issue #123) - 4567 (for issue #4567) How to find: Look at issue URL or title - URL: https://gitlab.com/group/project/-/issues/123 → use 123 - Title: "Fix login bug (#123)" → use 123 Note: This is NOT the global issue ID
max_lengthNoMaximum summary length Type: integer Range: 100-5000 Default: 500 Examples: - 300: Very concise summary - 500: Standard summary - 1000: Detailed summary Use case: Control output size for LLM context
Behavior4/5

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

With no annotations provided, the description carries full burden and does well: it describes the return format ('Condensed issue information'), smart truncation behavior ('Preserves key information, Removes redundancy, Fits context limits'), and includes contextual notes about what gets included (title, description, comments, status). It doesn't mention rate limits or authentication requirements, but provides substantial behavioral context.

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 clear sections (Returns, Use when, Includes, Smart truncation, Related tools) and uses bullet points efficiently. Every sentence earns its place, though the 'Smart truncation' section could be slightly more concise.

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?

For a tool with 3 parameters, 100% schema coverage, but no annotations and no output schema, the description provides good context: purpose, usage guidelines, behavioral traits, and sibling differentiation. It doesn't describe the exact output format or error conditions, but covers most essential aspects given the structured data available.

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 schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), baseline is 3 even with no param info in description.

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: 'Generate AI-friendly issue summary' with specific components included (title, description, comments, status). It distinguishes from sibling gitlab_get_issue by emphasizing condensed vs full details, and from gitlab_summarize_pipeline by specifying issue vs pipeline focus.

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

Usage Guidelines5/5

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

The description explicitly provides usage guidance: 'Use when: Processing issues with AI' and 'Related tools:' section names specific alternatives (gitlab_get_issue for full details, gitlab_summarize_pipeline for pipeline summaries). This gives clear when-to-use and when-not-to-use context.

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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