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VISHAL-MK

MCP Weather Server

by VISHAL-MK
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

MCP Weather Server

A simple MCP (Model Context Protocol) server built during an Agentic AI Internship. This server demonstrates how MCP can be used to provide tools, resources, and prompts to AI applications.

Features

MCP servers can provide the following functionalities:

Resources

File-like data that can be read by clients, such as API responses or file contents.

Tools

Functions that can be called by Large Language Models (LLMs) with user approval.

Prompts

Pre-written templates that help users accomplish specific tasks efficiently.

Related MCP server: Weather Service MCP

Requirements

  • Python 3.10 or higher

  • uv package manager

  • Python MCP SDK 1.2.0 or higher

  • httpx

Project Setup

1. Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Initialize the Project

uv init .

3. Install Dependencies

uv add "mcp[cli]" httpx

4. Create the Server File

Create a file named weather.py and add the MCP server implementation.

5. Run the Server

uv run weather.py

Project Structure

.
├── weather.py
├── pyproject.toml
├── uv.lock
├── README.md
└── .gitignore

Learning Outcomes

  • Understanding MCP architecture

  • Building custom MCP tools

  • Integrating APIs using Python

  • Running MCP servers with uv

  • Working with Agentic AI applications

Author

Vishal M K B.E. CSE (AI & ML)

Available Tools

1 tool
get_alertsA

Get weather alerts for a US state.

Args: state: Two-letter US state code (e.g. CA, NY)

ParametersJSON Schema
NameRequiredDescriptionDefault
stateYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the input and output type but lacks safety or error behavior details. For a simple read operation, it is adequate but could be improved.

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 is extremely concise, with a single sentence and a short argument explanation. Every part serves a clear purpose with no wasted words.

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

Completeness4/5

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

Given the tool's simplicity (1 parameter, no siblings, output schema present), the description provides the necessary information. It could elaborate on output format, but the output schema covers that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds significant value by specifying state as a two-letter US state code with examples. This compensates for the lack of schema documentation.

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 clearly states the tool retrieves weather alerts for a US state. It uses a specific verb and resource, and there are no sibling tools to differentiate, so purpose is unambiguous.

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 indicates the tool is for US states, providing clear context. No alternatives or when-not-to-use guidance is given, but the scope is well-defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedget_alerts
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TDQS

A4/5.0
Disambiguation5/5

With only one tool, there is no ambiguity. The tool name and description clearly convey its purpose.

Naming Consistency5/5

The single tool uses a clear verb_noun pattern (get_alerts), which is consistent and appropriate.

Tool Count2/5

One tool is significantly fewer than expected for a weather server, which typically includes forecasts, current conditions, and other weather data. The server seems under-scoped.

Completeness2/5

The server only provides alerts for US states, missing core weather functionalities like forecasts, current conditions, or radar. This is a severe gap for a weather server.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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