Save hours every week. This arch check tool toolkit gives you a battle-tested prompt and operating playbook for restaurant/brand growth. No setup, works in any LLM chatbot.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional user query to scope the analysis. |
Changes observed during successful MCP inspections.
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 read/write behavior, side effects, permissions, output format, or rate limits. The text is promotional and gives no operational context.
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?
The description is short but front-loaded with marketing ('Save hours every week') instead of purpose. Neither sentence helps an agent understand what to invoke or when.
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 low-complexity tool with one optional parameter and no output schema, the description still fails to explain what an 'arch check' produces or how it differs from related tools. The marketing framing leaves the core capability undefined.
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?
The input schema fully documents the single optional 'query' parameter, so the baseline is 3. The description adds no additional meaning about how the query scopes the analysis or what form it should take.
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 repeats the name ('arch check tool') and adds marketing claims about saving time and restaurant/brand growth, but it does not state a specific operation or what the tool actually checks. It does not distinguish the tool from siblings such as dt-arch-gen-tool or dt-arch-tpl-tool.
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?
There is no when-to-use guidance, no exclusions, and no explicit routing to alternatives. 'Works in any LLM chatbot' is a product claim rather than invocation guidance.
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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