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VayuChat MCP

VayuChat MCP

by nipunbatra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

plot_weekday_weekend

Visualize differences between weekday and weekend data patterns by generating comparison charts for analysis.

Instructions

Create weekday vs weekend comparison chart.

Args: value_column: Column to compare group_by: Grouping column (default: 'city') table: Table name title: Optional title

Returns: Base64 encoded plot.

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": "plot_weekday_weekend"
  }
}

Input Schema

NameRequiredDescriptionDefault
value_columnYes
group_byNocity
tableNoair_quality
titleNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • removedInput schema / properties / day_of_week_column
      Removed value: -{
-  "default": "day_of_week",
-  "type": "string"
-}
    • changedInput schema / properties / group_by / default
      Previous value: -nullNew value: +"city"
    • 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 of behavioral disclosure. While it mentions that the tool creates a chart and returns a Base64 encoded plot, it lacks critical details: it doesn't specify what type of chart is generated (e.g., bar, line), whether it modifies data, requires specific permissions, has rate limits, or handles errors. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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: the first sentence states the purpose clearly, followed by a structured 'Args' and 'Returns' section. Each sentence adds value without redundancy, making it efficient. However, the formatting with bullet-like sections could be slightly improved for readability, but it remains concise overall.

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 (4 parameters, 0% schema coverage, no annotations, but has an output schema), the description is partially complete. It covers the purpose and parameters but lacks behavioral details and usage guidelines. The output schema existence means return values are documented elsewhere, reducing the burden, but without annotations, more context on tool behavior is needed for full completeness.

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 lists parameters (value_column, group_by, table, title) and adds some meaning: value_column is 'Column to compare', group_by is 'Grouping column' with a default, table is 'Table name', and title is 'Optional title'. However, it doesn't fully explain semantics (e.g., what 'compare' entails, valid column types, table constraints), leaving gaps despite covering all parameters nominally.

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: 'Create weekday vs weekend comparison chart.' This specifies the verb ('create') and resource ('weekday vs weekend comparison chart'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'compare_weekday_weekend' or other plotting tools like 'plot_comparison', leaving room for potential confusion.

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. It doesn't mention sibling tools like 'compare_weekday_weekend' (which might serve a similar purpose) or other plotting tools such as 'plot_comparison', nor does it specify prerequisites, exclusions, or contextual cues for selection.

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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