Save hours every week. This eldercare risk guide lite 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?
With no annotations provided, the description carries the full disclosure burden and mostly fails it: it says nothing about what the tool returns, whether it mutates state, token/length limits, or permissions. The only behavioral hints are 'no setup' and 'works in any LLM chatbot', which convey deployment convenience but not 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
It is short, but every sentence is marketing copy ('Save hours every week', 'battle-tested') that does not earn its place by helping an agent decide or invoke. The front-loaded line is a benefit claim rather than the tool's function.
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 prompt/playbook-delivery tool with no annotations and no output schema, the description should explain what artifact comes back and how to use the query argument; it does none of that. The conflicting eldercare vs. restaurant/brand framing leaves the agent unable to judge relevance at all.
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?
There is a single optional parameter and schema coverage is 100%, so the schema already fully documents 'query' as an optional scoping input. The description adds no meaning about how a query scopes the analysis, so the baseline of 3 applies rather than a higher score.
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 calls this an 'eldercare risk guide lite toolkit' but then claims it delivers 'a battle-tested prompt and operating playbook for restaurant/brand growth' — the domain is internally inconsistent, so an agent cannot tell what resource it actually produces. It never states a verb+resource operation (e.g., 'returns a risk-assessment checklist'), only promotional framing.
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?
No when-to-use guidance, no when-not-to-use, and no mention of the obvious sibling 'eldercare-risk-guide' that presumably covers the full version. 'No setup, works in any LLM chatbot' is a sales claim, not a selection criterion for an agent.
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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