Save hours every week. This probation pass plan 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 disclosure burden, and it discloses nothing behavioral: no indication of what the tool returns (a prompt? a plan document? a checklist?), how output is structured, or what happens to the optional query. "Battle-tested" and "save hours" are marketing claims, not behavioral facts. For an unannotated tool this is a serious gap.
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 all three are promotional rather than informational, and the front-loaded sentence is a benefit claim ("Save hours every week") rather than the tool's function. Every sentence would need to be replaced, not trimmed, to make this useful.
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 and no output schema, the description is the only source of information about behavior and return values, and it omits both. An agent cannot tell what artifact this produces or how to use the result. The only usable signal is the domain hint (restaurant/brand growth).
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?
Schema coverage is 100% and the single optional `query` parameter is already documented in the schema as scoping the analysis. The description adds no meaning about how the query shapes the output or what a missing query yields. Baseline 3 is appropriate when the schema does the work and the description contributes nothing further.
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 says it is a "probation pass plan toolkit" offering a "battle-tested prompt and operating playbook for restaurant/brand growth," which never resolves into a concrete verb plus resource. The name suggests a workplace-probation plan while the text points at restaurant/brand growth, so the actual function is ambiguous. It gives no basis for distinguishing this from the many sibling playbook/prompt tools.
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?
The only contextual cue is "for restaurant/brand growth," which hints at a domain but not at when to invoke this instead of a sibling such as brand-fnb-playbook or restaurant-ops-diagnostic. There is no stated trigger, prerequisite, or exclusion. "No setup, works in any LLM chatbot" is a selling point, not usage 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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