Save hours every week. This talk script generator 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 it discloses almost nothing: it does not say what a call returns, whether it is a static prompt retrieval or a generation step, or what the 'operating playbook' contains. 'No setup, works in any LLM chatbot' is the sole behavioral claim, and it says nothing about the invocation itself.
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 opens with a marketing benefit ('Save hours every week') instead of front-loading what the tool does, which is the least useful framing for an agent deciding whether to call it. All three sentences are promotional and none of them is an operational statement.
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 promotional-only description, an agent has no basis for predicting the return value or the effect of calling this tool. The description should compensate for the missing structured metadata and does not.
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 one optional parameter with 100% schema description coverage ('Optional user query to scope the analysis'), so the schema already documents it fully. Baseline 3 applies; the description adds no additional meaning about how the query influences output.
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 name: 'talk script generator toolkit' with 'prompt and operating playbook.' It never says what the tool actually does when invoked — whether it returns a prompt template, a generated script, or guidance — and with ~15 sibling *-talk-kit tools (dy-talk-kit, gzh-talk-kit, sph-talk-kit, xhs-talk-kit, zh-talk-kit, live-talk-script), the only differentiator offered, 'restaurant/brand growth,' is too vague to route on.
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 hint is the domain phrase 'restaurant/brand growth'; there is no statement of when to pick this over the many sibling talk-kit tools, no prerequisites, and no exclusions. 'Works in any LLM chatbot' describes the artifact, not the conditions for calling the tool.
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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