Supports direct integration with LangChain through langchain-groq, enabling programmatic consumption of weather data in client applications
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Weather MCP Server what's the weather alert for Los Angeles?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A simple demo of MCP python sdk.
start a new project
uv init <project-name>create a virtual environment
uv venvactivate the virtual environment
uv venv --activateinstall the dependencies
uv add <package>
uv add -r requirements.txt
uv syncrun application
uv run app.pyrun the development server
uv run devbuild the project
uv run buildRun server using MCP Inspector :
Install MCP python sdk
uv add "mcp[cli]"Run mcp inspector
uv run mcp dev server/weather.pyMCP Inspector is up and running at http://127.0.0.1:6274
Add server to claude desktop :
run command
uv run mcp install .\server\weather.pyquery "what is weather alert in CA" in claude desktop
Add server to cursor :
goto preferences > settings > mcp and copy paste claude config
{
"mcpServers": {
"weather": {
"command": "uv",
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"C:\\Users\\Suraj\\Desktop\\Python\\mcp-crash-course\\mcpcrashcourse\\server\\weather.py"
]
}
}
}Then do a query in cursor chat
Directly consume server in code - using mcp-use :
Install mcp-use
uv add mcp-useInstall langchain-groq
uv add langchain-groqrun client
uv run server/client.pyGet weather alerts for US state Args: state: Two letter US state code (e.g CA, NY etc)
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
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 the tool 'Get weather alerts' but does not describe behavioral traits such as rate limits, authentication needs, error handling, or what the return format looks like. For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences, but it is not optimally structured. The first sentence states the purpose, and the second explains the parameter, but it could be more front-loaded with key details. It avoids waste but lacks polish in organization.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (simple tool with one parameter), no annotations, and no output schema, the description is incomplete. It covers the purpose and parameter semantics but misses behavioral context, usage guidelines, and output details. For a tool with no structured support, more comprehensive description is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that the 'state' parameter is a 'Two letter US state code (e.g CA, NY etc)', which clarifies the format and provides examples. This is valuable beyond the basic schema, though it could be more detailed (e.g., list of valid codes).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get weather alerts for US state' specifies the verb ('Get'), resource ('weather alerts'), and geographic scope ('US state'). It distinguishes the tool's function well, though without sibling tools, full differentiation isn't tested. The purpose is specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, prerequisites, or context for invocation. It only states what the tool does without indicating scenarios, limitations, or comparisons to other tools. This leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_alertsScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently follows a consistent naming pattern. The tool name 'get_alerts' uses a clear verb_noun format, which would be consistent if more tools were added.
A single tool is too few for a weather server, as it lacks basic operations like getting current conditions, forecasts, or historical data. This severely limits the server's utility and scope.
The tool set is severely incomplete for a weather domain. It only provides alerts for US states, missing essential functions such as retrieving current weather, forecasts, or data for locations outside the US, making it inadequate for typical weather-related tasks.
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
The official Model Context Protocol server for Ambee. It gives any MCP-compatible AI assistant — Claude, ChatGPT, Cursor, VS Code, Ollama, and more direct access to live air quality, pollen, and weather data. To get started, including information on signing up and obtaining your Ambee key, check out the Ambee documentation on https://docs.ambeedata.com
An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
