Return 24 hourly demand buckets for a business (from completed reservation history). Used to surface busy windows and optimize pricing/staffing. Owner-gated. This MCP tool makes a real HTTP call to the canonical Pabandi endpoint and returns the actual platform response.
| Name | Required | Description | Default |
|---|---|---|---|
| businessId | Yes | business id |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description bears the full burden of transparency. It explicitly mentions the data source (completed reservation history), the 'Owner-gated' requirement, and that it is a live HTTP call to the canonical endpoint, providing critical operational context.
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?
The description is highly efficient, using three concise sentences to cover the WHAT, WHY, and HOW (gating/endpoint behavior) in a logical flow, adding zero unnecessary fluff.
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?
Given that the tool provides a specific business outcome (24 hourly buckets) and operational constraints (Owner-gated, live API call), the definition is functionally complete for an AI agent to attempt a call correctly.
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?
With 100% schema coverage, the schema already fully documents the required 'businessId' parameter. The description adds no further semantic value regarding the input format or meaning beyond what is structured.
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 explicitly states the function (return 24 hourly demand buckets), the source data (completed reservation history), and the ultimate goal (pricing/staffing optimization). It is a highly specific forecasting tool.
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?
It clearly defines the tool's use case—optimizing pricing/staffing—which implies when it should be used. The 'Owner-gated' status is a crucial usage context, although it does not explicitly list alternative tools for demand prediction.
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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