Save hours every week. This livestream ops coach 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 exist, so the description carries the full behavioral burden, yet it discloses nothing about what the call returns, whether the optional query scopes or replaces the output, or how the prompt/playbook is delivered. Phrases like "Save hours every week" and "battle-tested" are promotional claims, not behavior. For a zero-annotation tool this leaves the agent blind.
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 the first and third ("Save hours every week", "No setup, works in any LLM chatbot") are pure sales copy that consumes the front-loaded position without conveying function. The lead sentence is spent on a benefit claim rather than what the tool does.
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 sole source of behavioral and return-value information, and it supplies neither. For a tool whose entire value is the shape of the artifact it returns, an agent cannot predict the result before invoking it.
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 only one optional parameter and schema coverage is 100%, with the schema itself stating it scopes the analysis. Baseline 3 applies since the schema already documents it. The description adds no meaning about how the query influences the returned playbook, so it does not exceed the baseline.
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 restates the tool name with marketing framing ("livestream ops coach toolkit") and only vaguely hints that it delivers a prompt/playbook for "restaurant/brand growth." It never states a concrete verb showing what the tool actually does with a call. With many near-identical siblings (live-script, live-room-script, live-selling-script, dy-live-kit, gzh-live-kit), it provides no way to tell them apart.
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, when-not-to-use, or alternative-tool guidance at all. The only conditional language ("No setup, works in any LLM chatbot") describes portability, not tool selection. An agent has nothing to route on.
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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