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VayuChat MCP

VayuChat MCP

by nipunbatra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

plot_hourly_pattern

Visualize hourly data trends from tables to identify patterns over time. Specify value column, optional grouping, and table name to generate time-based plots.

Instructions

Plot hourly patterns.

Args: value_column: Column to plot group_by: Optional grouping column 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_hourly_pattern"
  }
}

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
    • changedInput schema / properties / group_by / default
      Previous value: -nullNew value: +"city"
    • removedInput schema / properties / hour_column
      Removed value: -{
-  "default": "hour",
-  "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

C2.7/5.0
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. It mentions that the tool returns a 'Base64 encoded plot', which is useful, but lacks critical details: it doesn't specify the plot type (e.g., line chart, bar chart), default behaviors (e.g., how 'group_by' affects the plot), error handling, or any performance considerations. For a plotting tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 'Plot hourly patterns', followed by a structured 'Args' and 'Returns' section. Every sentence adds value: the first states the purpose, and the subsequent lines document parameters and output. There's no wasted text, making it efficient and easy to scan.

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 complexity (a plotting tool with 4 parameters, no annotations, and an output schema that likely defines the Base64 string), the description is minimally complete. It covers the basic purpose, parameters, and return type, but lacks details on plot specifics, usage context, and behavioral traits. The presence of an output schema means it doesn't need to explain return values further, but overall, it's adequate with clear gaps in guidance and transparency.

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 the parameters ('value_column', 'group_by', 'table', 'title') and adds some meaning by noting 'Column to plot' and 'Optional grouping column', but it doesn't explain the semantics deeply (e.g., what 'value_column' represents, how 'group_by' influences the plot, what 'table' refers to). With 4 parameters and low schema coverage, this provides basic clarification but falls short of fully compensating for the lack of schema descriptions.

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

Purpose3/5

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

The description states the tool 'Plot hourly patterns' which indicates a visualization function, but it's vague about what specifically constitutes 'hourly patterns' (e.g., trends, distributions, averages). It distinguishes from some siblings like 'analyze_correlation' or 'describe_table' by focusing on plotting, but doesn't clearly differentiate from similar plotting tools like 'plot_time_series' or 'plot_comparison' in terms of what makes 'hourly' patterns unique.

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 prerequisites, such as needing time-based data, or compare it to siblings like 'plot_time_series' or 'plot_weekday_weekend' that might also handle temporal data. There's no indication of when this tool is preferred or when other tools might be more appropriate.

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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