list_annotation_issuesList the tracker (GitHub) issues opened from an annotation, with each issue’s live state. Set refresh to reconcile against GitHub (open/closed + any linked pull request).
| Name | Required | Description | Default |
|---|---|---|---|
| refresh | No | Reconcile live state from GitHub. Optional. | |
| annotation_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| issues | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds context about listing with live state and the refresh reconciliation behavior, which enhances transparency without contradiction.
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 sentences, front-loaded with the main purpose, no extraneous words. Every part adds value.
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?
Given the presence of an output schema and clear annotations, the description sufficiently covers the tool's functionality, including the refresh parameter's effect. It could be slightly more explicit about the expected output, but overall complete.
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 50% with only refresh having a description. The description adds meaningful information for refresh (reconcile against GitHub open/closed + linked PR), and annotation_id is minimally documented but its role is implied by the tool name. The description partially compensates for the coverage gap.
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 uses a specific verb 'list' and resource 'tracker (GitHub) issues opened from an annotation' with a clear scope, and it distinguishes from siblings like create_github_issue and list_project_issues.
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 indicates when to use the refresh parameter but does not explicitly provide when to use this tool vs alternatives or when not to use it. It implicitly differentiates by naming the resource but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of annotation or project management. Complementing pairs like diagnose_annotation vs. generate_prompt and get_annotation_analysis vs. diagnose_annotation are clearly differentiated by descriptions. No two tools overlap in purpose.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_annotations, create_github_issue, get_activity). Verbs like get, list, create, add, update are used systematically, ensuring predictability.
With 17 tools, the server covers annotation operations, project metrics, comments, GitHub integration, and sharing without being overwhelming. The scope is well-scoped for a specialized feedback/annotation tool.
Core annotation workflows (list, get, update status, comment, diagnose, generate prompt, create issues) are covered. Missing annotation creation, deletion, and detail updates are minor gaps given the server's focus on post-creation analysis and workflow.