Vynix

Create GitHub issue

create_github_issue

Create a GitHub issue from an annotation. Uses the project’s configured repo and assignee unless overridden. Embeds the generated AI prompt in the issue body.

Input Schema

TableJSON Schema

Name	Required	Description
`repo`	No	owner/repository to file the issue in. Optional.
`assignees`	No	GitHub usernames to assign. Optional.
`annotation_id`	Yes	The id of the annotation to file as an issue.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	No
`repo`	No
`state`	No
`provider`	No
`issue_url`	No
`created_at`	No
`issue_number`	No
`annotation_id`	No

TDQS

A4.4/5.0

Behavior4/5

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

Annotations show it's a write (readOnlyHint=false). Description adds that it embeds the generated AI prompt in the issue body, which is behavioral context beyond annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no wasted words. Front-loaded with purpose, then key behaviors.

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?

With output schema present, description provides sufficient context for a simple create tool. Explains core functionality and defaults. Could mention authentication prerequisites but not required given context signals.

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 100%. Description adds context: repo and assignees can be overridden from project defaults, and annotation_id is used. This adds meaning beyond parameter types.

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?

States a specific verb ('Create') and resource ('GitHub issue from an annotation'), and distinguishes from sibling list tools. The description indicates this tool creates rather than lists issues, making its purpose clear.

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

Usage Guidelines4/5

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

Provides context: uses project's configured repo/assignee unless overridden, and embeds AI prompt. This implies when to use (file an annotation as issue) and hints at overrides, but no explicit when-not or alternatives listed.

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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TDQS

A3.9/5.0

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Create GitHub issue

Input Schema

Output Schema

TDQS

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TDQS

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