resolve_issueMark an issue fixed, or reopen it. Fixing restores whatever mark was underneath — a check that was passing before the issue goes back to passing, and one that was never tested goes back to untested.
| Name | Required | Description | Default |
|---|---|---|---|
| by | Yes | Who is making the change. | |
| fixed | No | true to fix (default), false to reopen. | |
| issue_id | Yes | Issue id from list_issues or get_check. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds a non-obvious behavioral detail beyond the annotations: fixing 'restores whatever mark was underneath,' with concrete examples. The destructiveHint annotation already signals mutation, and the description usefully explains what changes happen to the underlying check state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The core action is front-loaded, and the explanatory example is tightly scoped to the most important behavioral nuance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple three-parameter mutation tool with no output schema, the description covers the key behavior, the target issue source is documented in the schema, and the toggling semantics are clear. It omits permission requirements and return details, but those are not critical given the annotations and schema richness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3, but the description adds meaning to the `fixed` parameter by explaining that setting an issue as fixed is not a simple boolean flip—it restores the prior mark. This deepens the agent's understanding beyond the schema's 'true to fix (default), false to reopen.'
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Mark an issue fixed, or reopen it.' It also clarifies the dual behavior, which distinguishes this status-toggle tool from generic issue-editing tools like edit_issue.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to invoke the tool: to fix or reopen an issue. It does not explicitly name alternatives or state when not to use it, but the purpose is unmistakable and there are no misleading instructions.
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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