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VayuChat MCP

VayuChat MCP

by nipunbatra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

compare_weekday_weekend

Analyze how metrics differ between weekdays and weekends to identify patterns in time-based data.

Instructions

Compare weekday vs weekend values for a metric.

Args: value_column: Column to compare (e.g., 'PM2.5', 'PM10') group_by: Optional grouping column (e.g., 'city') table: Table name (default: air_quality)

Returns: Comparison statistics.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "value_column": "<value_column>"
    },
    "name": "compare_weekday_weekend"
  }
}

Input Schema

NameRequiredDescriptionDefault
value_columnYes
group_byNo
tableNoair_quality

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • removedInput schema / properties / date_column
      Removed value: -{
-  "default": "date",
-  "type": "string"
-}
    • removedInput schema / properties / day_of_week_column
      Removed value: -{
-  "default": "day_of_week",
-  "type": "string"
-}
    • removedInput schema / properties / name
      Removed value: -{
-  "type": "string"
-}
    • addedInput schema / properties / table
      Added value: +{
+  "default": "air_quality",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "name",
-  "value_column"
-]New value: +[
+  "value_column"
+]
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions what the tool does but lacks critical behavioral details: it doesn't specify what statistical methods are used for comparison, whether it modifies data, what permissions are needed, or how results are structured beyond 'comparison statistics'. For a tool with no annotations, this is insufficient disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose in the first sentence. The Args and Returns sections are structured but could be more integrated. There's minimal waste, though the formatting as separate sections slightly reduces flow. Overall, it's efficient but not perfectly polished.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is minimally adequate. The output schema likely covers return values, reducing the need for detailed return explanations. However, with no annotations and incomplete parameter semantics, it leaves gaps in behavioral context and usage guidance, making it just viable but not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds some meaning by providing examples for 'value_column' and 'group_by' (e.g., 'PM2.5', 'city') and clarifies the default for 'table'. However, it doesn't fully explain parameter constraints or interactions, leaving gaps like valid column names or grouping effects. This partial compensation earns a baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Compare weekday vs weekend values for a metric.' This specifies the verb ('compare'), resource ('weekday vs weekend values'), and scope ('for a metric'). However, it doesn't explicitly differentiate from sibling tools like 'plot_weekday_weekend' or 'compare_cities', which would require a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There are multiple sibling tools that might overlap (e.g., 'plot_weekday_weekend', 'compare_cities', 'query_table'), but no explicit when/when-not instructions or named alternatives are mentioned. The context is implied but not articulated.

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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