Save hours every week. This gzh cover kit 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. It hints that this is a static prompt/playbook with no setup, but it never states what a call returns, whether it mutates anything, whether there are platform or auth constraints, or how the 'playbook' is delivered.
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 sentences lead with an unverifiable benefit claim ('Save hours every week') rather than the function, and 'gzh cover kit toolkit' is redundant. The space spent on sales copy would be better used on what the tool returns.
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 one-parameter tool with no annotations and no output schema, the description should at minimum describe the return artifact and scope of the playbook. Instead it offers only promotional framing, leaving an agent unable to predict the result of calling 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?
Schema description coverage is 100% — the single optional 'query' parameter is documented as scoping the analysis. The description adds nothing about how the query is interpreted, which is acceptable at baseline 3 given full schema coverage.
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 never says what the tool actually produces. It claims to give 'a battle-tested prompt and operating playbook for restaurant/brand growth,' which restates the name ('gzh cover kit toolkit') in marketing terms without naming the artifact (e.g., WeChat official-account cover copy/title variants). Compared with siblings like dy-cover-kit, sph-cover-kit, xhs-cover-kit, zh-cover-kit, there is zero differentiation of platform or output.
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 statement, no exclusion, and no named alternative among the many cover-kit siblings. 'No setup, works in any LLM chatbot' is a portability claim, not guidance about when an agent should pick this tool.
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.