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VayuChat MCP

VayuChat MCP

by nipunbatra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

plot_time_series

Visualize time series data from datasets to identify trends and patterns over time. Generate plots with optional grouping by categories like city or region for comparative analysis.

Instructions

Create a time series plot.

Args: value_column: Column to plot group_by: Optional column for separate lines (e.g., '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_time_series"
  }
}

Input Schema

NameRequiredDescriptionDefault
value_columnYes
group_byNo
tableNoair_quality
titleNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • removedInput schema / properties / date_column
      Removed value: -{
-  "default": "date",
-  "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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool creates a plot and returns Base64 encoded output, which covers the basic operation. However, it lacks critical behavioral details: whether this is a read-only operation, what permissions are needed, whether it modifies data, how errors are handled, or any rate limits. For a tool with no annotation coverage, this is insufficient.

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 well-structured with clear sections (Args, Returns) and uses bullet points efficiently. Each sentence adds value: the purpose statement, parameter explanations, and return format. However, the 'Create a time series plot' line is somewhat redundant with the tool name, and the parameter explanations could be more concise.

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, no annotations, 0% schema coverage, but has output schema), the description is moderately complete. It covers the purpose, parameters, and return format. However, it lacks behavioral context, usage guidelines, and detailed parameter constraints. The output schema existence means the description doesn't need to explain return values, but other gaps remain significant.

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 schema provides no parameter documentation. The description adds semantic meaning for all 4 parameters, explaining what each represents (e.g., 'value_column: Column to plot', 'group_by: Optional column for separate lines'). However, it doesn't provide format details, constraints, or examples beyond basic definitions. This partially compensates for the schema gap but leaves implementation details unclear.

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 as 'Create a time series plot' with a specific verb ('create') and resource ('time series plot'). It distinguishes from siblings like 'plot_comparison' or 'plot_hourly_pattern' by specifying the plot type. However, it doesn't explicitly differentiate from all siblings (e.g., 'plot_funding_trend' might also create time series plots).

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 when this tool is appropriate, what prerequisites exist, or how it differs from sibling tools like 'plot_funding_trend' or 'plot_hourly_pattern' that might also create visualizations. The agent must infer usage from the tool name alone.

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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