Save hours every week. This retro tpl 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 supplied, so the description carries the full behavioral burden and largely fails it: it never says whether the tool generates text, returns a template, or what the caller should expect back. The only concrete behavioral hint is "No setup, works in any LLM chatbot," which is thin for a tool with zero annotation coverage.
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?
Three short sentences, but they are front-loaded with hype ("Save hours every week") rather than the tool's purpose. Nothing here earns its place over a single plain sentence stating what the template produces.
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?
With no annotations, no output schema, and a vague one-parameter contract, the description should explain what the tool returns and how it differs from its check and gen siblings. Instead it supplies only promotional copy, leaving an agent unable to predict the result of a call.
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 single optional parameter is fully documented in the schema (100% coverage), so the schema already does the work and the baseline of 3 applies. The description adds nothing about how "query" scopes the analysis, but it is not required to given the schema quality.
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 mostly restates the tool name ("retro tpl tool toolkit") and never explains what the tool actually does operationally. It gestures at "a battle-tested prompt and operating playbook for restaurant/brand growth," which is marketing framing rather than a verb+resource statement, and it does not distinguish this tool from the sibling pm-retro-check-tool or pm-retro-gen-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 at all, and no mention of the check/gen siblings that an agent must choose between. "No setup, works in any LLM chatbot" is a sales claim, not a selection criterion.
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.