Save hours every week. This mkt scene library 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 disclosure burden, and it spends that space on marketing claims ("Save hours every week") instead of behavior. It never states what the tool actually returns, how the playbook is scoped, or anything about limits or side effects.
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 they are promotional copy rather than a front-loaded statement of purpose. Nothing in the opening tells an agent what capability is being invoked, so brevity here reflects missing content rather than efficiency.
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 output schema and only one optional input, the description is the sole place an agent could learn what a "scene library" yields and how results are shaped. It answers neither question, leaving the tool effectively opaque relative to its siblings.
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 with 100% schema description coverage ("Optional user query to scope the analysis"), so the schema already does the explaining. The description adds no further meaning about how the query scopes results, which is the baseline-3 case when coverage is high.
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 the toolkit gives a "battle-tested prompt and operating playbook for restaurant/brand growth," which hints at content but never names a concrete operation or resource. Against siblings like mkt-campaign-library, mkt-problem-library, mkt-situation-library, and mkt-sop-core, "mkt scene library" is not distinguished in any way an agent could act 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?
There is no guidance on when to call this tool, when not to, or which sibling library to pick instead. "No setup, works in any LLM chatbot" is an adoption pitch rather than a usage condition.
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.