Vynix

Run AI diagnosis

diagnose_annotation

Run the AI Diagnosis Engine on an annotation: it analyses the captured page, element, DOM, and runtime errors to produce ranked root causes, a confidence score, a suggested fix, and the likely source files. Calls an AI provider (uses tokens / may cost money) and stores the result. Requires AI + diagnosis enabled by the workspace admin and a key set.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Override the model. Optional.
`provider`	No	Override the AI provider. Defaults to the workspace default.
`annotation_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	No
`model`	No
`status`	No
`summary`	No
`provider`	No
`confidence`	No
`created_at`	No
`updated_at`	No
`root_causes`	No
`annotation_id`	No
`suggested_fix`	No
`suggested_files`	No

TDQS

A4.2/5.0

Behavior4/5

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

Beyond annotations (readOnlyHint=false, idempotentHint=false), the description adds key behavioral details: it calls an external AI provider, may cost money/use tokens, and stores the result. This informs agents of side effects and costs.

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 sentences, front-loaded with purpose and outputs. Every sentence adds value, no fluff. Ideal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (AI inference, multiple inputs, output schema), the description covers inputs, processing, outputs, and prerequisites (admin settings). The output schema handles return values, so no further detail needed.

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

Parameters2/5

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

Schema coverage is 67% (model and provider have descriptions; annotation_id has none). The description does not clarify any parameter; annotation_id is only implicitly referenced. It adds minimal value beyond the schema, missing an opportunity to document the missing parameter.

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 it runs an AI diagnosis engine on an annotation, specifying what it analyzes (page, element, DOM, runtime errors) and outputs (root causes, confidence, fix, source files). This uniquely distinguishes it from sibling tools like get_annotation or list_annotations.

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?

The description includes prerequisites (AI + diagnosis enabled by admin, key set) and implies use for deep analysis. While it doesn't explicitly state when not to use or name alternatives, the context is clear enough for an agent to decide.

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.

Run AI diagnosis

Input Schema

Output Schema

TDQS

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TDQS

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