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saidsef

GitHub PR Issue Analyser

by saidsef

update_issue

Modify an existing GitHub issue by updating its title, body, labels, and state. Provide repository owner, name, issue number, and new content.

Instructions

Updates an existing issue in the specified GitHub repository. Args: repo_owner (str): The owner of the repository. repo_name (str): The name of the repository. issue_number (int): The number of the issue to update. title (str): The new title for the issue. body (str): The new body content for the issue. labels (list[str], optional): A list of labels to assign to the issue. Defaults to an empty list. state (str, optional): The state of the issue ('open' or 'closed'). Defaults to 'open'. Returns: Dict[str, Any]: The updated issue data as returned by the GitHub API if the update is successful. None: If an error occurs during the update process. Error Handling: Logs an error message and prints the traceback if the request fails or an exception is raised.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_ownerYes
repo_nameYes
issue_numberYes
titleYes
bodyYes
labelsNo
stateNoopen

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
numberYes
titleYes
bodyYes
stateYes
userYes
created_atYes
updated_atYes
labelsYes
Behavior3/5

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

No annotations, so description carries full burden. It discloses update behavior, return types, and error handling. However, it does not specify whether updating state to 'closed' closes the issue, or that omitted optional fields remain unchanged. Some behavioral details are missing.

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 sections (Args, Returns, Error Handling) and front-loads the main purpose. It is fairly concise for 7 parameters, though it could be slightly more compact.

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?

Given 7 parameters, no annotations, and presence of an output schema, the description covers all parameters, return type, and error handling. It lacks detail on the return structure, but the output schema provides that. It is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description lists each parameter with a brief explanation (e.g., repo_owner: owner of repository). It clarifies optional parameters and defaults, adding meaning beyond type. However, it could explain the effect of 'state' more explicitly.

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 updates an existing issue in a GitHub repository, with a specific verb and resource. It distinguishes from siblings like create_issue (creates new) and update_assignees (updates only assignees).

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 explicit guidance on when to use this tool versus alternatives. It does not mention that create_issue should be used for new issues or that update_assignees is for assignee-only changes. The description lacks usage 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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