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sanhao-global-experts

ticket-diag-tool

AI-powered restaurant & brand intelligence pack. Instantly generate a ready-to-use ticket diag tool workflow - no prompt engineering needed. Ships as a full prompt kit with step-by-step instructions and a 30-day support promise.

Input Schema

NameRequiredDescriptionDefault
queryNoOptional user query to scope the analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

D1.7/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about what the tool does with the query, what it returns, latency, cost, or side effects. The sentences present are commercial framing ('ships as a full prompt kit', '30-day support promise') and convey zero operational behavior.

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

Conciseness2/5

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

Three short sentences, but all of them are promotional and none are front-loaded with the tool's function. Every sentence would need to be replaced rather than trimmed, so the brevity reflects missing content rather than efficiency.

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

Completeness1/5

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

A diagnostic tool with no annotations and no output schema requires the description to explain what gets diagnosed, what evidence is needed, and what the caller receives. None of that is present, and the sibling-name collisions (analyze/board/formula/tpl variants) make the omission fatal for correct selection.

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

Parameters3/5

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

Schema description coverage is 100% with a single optional 'query' parameter, so the schema already defines the only input and the baseline of 3 applies. The description adds no meaning about how the query scopes the analysis, but it also does not contradict the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description is marketing copy ('AI-powered restaurant & brand intelligence pack', 'full prompt kit with step-by-step instructions') that restates the tool name as a 'ticket diag tool workflow' rather than stating what the tool actually does. It never says whether it diagnoses tickets, generates a diagnostic report, or returns a template, and gives no way to distinguish it from ticket-analyze-tool, ticket-board-tool, ticket-formula-tool, or ticket-tpl-tool.

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

Usage Guidelines1/5

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

There is no when-to-use guidance, no prerequisites, and no mention of any alternative or sibling tool. 'No prompt engineering needed' and a '30-day support promise' are sales claims, not invocation guidance, leaving the agent with no basis for choosing this tool over the other ticket-family tools.

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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