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VayuChat MCP

VayuChat MCP

by nipunbatra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

show_table

Display specific rows and columns from data tables to explore datasets for analysis. Use this tool to view table contents with customizable parameters.

Instructions

Display rows from a table.

Args: name: Table name (air_quality, funding, city_info) rows: Number of rows to show (default: 10) columns: Optional list of columns to display

Returns: Formatted table data.

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": {
      "name": "<name>"
    },
    "name": "show_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYes
rowsNo
columnsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

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. It mentions that the tool displays data and returns formatted table data, but it doesn't cover important aspects like whether this is a read-only operation, if there are rate limits, authentication requirements, or how errors are handled. For a data retrieval tool with zero annotation coverage, this is a significant gap.

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 and appropriately sized, with a clear purpose statement followed by parameter explanations and return information. It uses bullet-like formatting under 'Args' and 'Returns' for readability. Every sentence adds value, and there's no redundant information, 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 that there is an output schema (which handles return values), the description doesn't need to detail output specifics. However, with no annotations and 0% schema description coverage, it partially compensates by explaining parameters. It's adequate for a simple data display tool but lacks behavioral context and usage guidelines, making it incomplete for optimal agent decision-making.

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 includes an 'Args' section that explains each parameter: 'name' as the table name with examples, 'rows' as the number of rows with a default, and 'columns' as an optional list. However, with 0% schema description coverage, the schema provides no additional details. The description compensates somewhat by adding examples and defaults, but it doesn't fully clarify data types or constraints beyond what's implied, leaving room for improvement.

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: 'Display rows from a table.' It specifies the verb 'display' and resource 'rows from a table,' making it easy to understand. However, it doesn't explicitly differentiate from siblings like 'list_tables' (which likely lists table names) or 'query_table' (which might allow more complex queries), leaving some ambiguity.

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 siblings such as 'list_tables' for listing table names or 'query_table' for more complex queries, nor does it specify prerequisites like needing to know table names beforehand. This lack of context makes it harder for an agent to choose correctly among similar tools.

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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