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VayuChat MCP

VayuChat MCP

by nipunbatra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

execute_code

Execute Python code to analyze air quality, funding, and city data using pandas, numpy, and matplotlib for custom calculations and visualizations.

Instructions

Execute custom Python code for advanced analysis.

Available variables:

  • air_quality, funding, city_info: DataFrames

  • pd, np, plt: Libraries

Args: code: Python code to execute

Returns: Output from code execution.

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": {
      "code": "<code>"
    },
    "name": "execute_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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 discloses available variables (DataFrames and libraries) and the return behavior, but lacks critical details: it doesn't mention security implications (e.g., sandboxing), execution limits (e.g., timeouts), error handling, or data mutation risks. For a code execution tool with zero annotation coverage, this is a significant gap in behavioral context.

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 stated first. The bullet points for variables and libraries are efficient, and the Args/Returns sections are clear. However, the 'Available variables' section could be integrated more seamlessly, and some redundancy exists (e.g., 'Returns' repeats 'Output from code execution').

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 (code execution with data access) and lack of annotations, the description is incomplete. It covers purpose and available resources but misses safety, limits, and error details. The output schema exists, so return values needn't be explained, but critical behavioral aspects are omitted, making it adequate only for basic use cases.

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?

The description adds minimal meaning beyond the input schema. It states the 'code' parameter is 'Python code to execute', which the schema (with 0% description coverage) doesn't specify. However, it doesn't elaborate on syntax, constraints, or examples. With one parameter and low schema coverage, the description partially compensates but remains basic, aligning with the baseline for moderate coverage scenarios.

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: 'Execute custom Python code for advanced analysis.' It specifies the verb ('Execute') and resource ('custom Python code'), and distinguishes it from sibling tools by emphasizing custom code execution rather than predefined analyses. However, it doesn't explicitly differentiate from siblings like 'query_table' or 'analyze_correlation' beyond the custom code aspect.

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 explicit guidance on when to use this tool versus alternatives. It mentions 'advanced analysis' but doesn't specify contexts, prerequisites, or exclusions. Given siblings like 'analyze_correlation' and 'plot_time_series', the agent lacks clear criteria for choosing this tool over others for similar tasks.

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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