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MCP Learning Server

MCP Learning Server

by somyabhadada
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

weather

Get mock weather data for an Indian city. Supports Jaipur, Delhi, Mumbai, Bangalore, Chennai, Kolkata, Hyderabad, and Pune.

Instructions

Get mock weather data for an Indian city. Supported cities: Jaipur, Delhi, Mumbai, Bangalore, Chennai, Kolkata, Hyderabad, Pune.

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": {
      "city": "<city>"
    },
    "name": "weather"
  }
}

Input Schema

NameRequiredDescriptionDefault
cityYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full behavioral burden. It discloses the mock nature of the data but lacks details about error handling, rate limits, or what happens with unsupported cities. A 3 is adequate for a simple read tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of two concise, front-loaded sentences. Every sentence adds value—the first states the tool's purpose, the second provides actionable city options.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, return values are documented. The tool is simple with one required parameter and no nested objects. The description fully covers usage context with the city list.

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 coverage is 0%, so the description must compensate. It implicitly covers the 'city' parameter by listing supported cities, adding meaning beyond the schema's bare type. However, it doesn't explain parameter format or optional details.

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

Purpose5/5

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

The description uses a specific verb 'Get' and resource 'mock weather data' with a clear scope 'for an Indian city'. It distinguishes itself from siblings (calculator, current_time, etc.) which are unrelated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists supported cities, providing context. No explicit when-to-use or when-not-to-use is needed since siblings are unrelated. A score of 4 reflects clear context without exclusions.

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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