Click on "Install 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 forecast for New York this weekend?"
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.
We’ll build a server that exposes two tools: get_alerts and get_forecast. Then we’ll connect the server to an MCP host (in this case, Claude for Desktop): Core MCP Concepts MCP servers can provide three main types of capabilities: Resources: File-like data that can be read by clients (like API responses or file contents) Tools: Functions that can be called by the LLM (with user approval) Prompts: Pre-written templates that help users accomplish specific tasks
Prerequisite knowledge This quickstart assumes you have familiarity with: Python LLMs like Claude Logging in MCP Servers When implementing MCP servers, be careful about how you handle logging: For STDIO-based servers: Never write to standard output (stdout). This includes: print() statements in Python console.log() in JavaScript fmt.Println() in Go Similar stdout functions in other languages Writing to stdout will corrupt the JSON-RPC messages and break your server. For HTTP-based servers: Standard output logging is fine since it doesn’t interfere with HTTP responses. Best Practices Use a logging library that writes to stderr or files.
Get weather alerts for a US state.
Args: state: Two-letter US state code (e.g. CA, NY)
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves alerts but doesn't describe what the alerts contain, their format, whether the data is real-time or historical, any rate limits, authentication needs, or error conditions. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 efficiently structured and front-loaded: the first sentence states the purpose, followed by a clear 'Args:' section with parameter details. Every sentence earns its place, with no redundant or verbose language. It's appropriately sized for a single-parameter tool.
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 tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose and parameter semantics adequately. However, the lack of behavioral details (e.g., data freshness, error handling) and usage guidelines relative to siblings prevents a perfect score.
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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains that 'state' is a 'Two-letter US state code' and provides examples ('e.g. CA, NY'), clarifying the parameter's format and expected values. This fully compensates for the schema's lack of documentation, making the parameter semantics clear.
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 a US state.' It specifies the verb ('Get'), resource ('weather alerts'), and geographic scope ('US state'), making the function unambiguous. However, it doesn't explicitly differentiate from its sibling 'get_forecast' (which presumably provides forecasts rather than alerts), preventing a perfect score.
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 like 'get_forecast'. It doesn't mention any prerequisites, exclusions, or contextual cues for choosing between siblings. The only implicit usage hint is the geographic limitation to US states, but this is part of the purpose rather than comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get weather forecast for a location.
Args: latitude: Latitude of the location longitude: Longitude of the location
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes |
| Name | Required | Description |
|---|---|---|
| result | 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 states what the tool does but doesn't describe any behavioral traits such as rate limits, authentication needs, error handling, or what the forecast includes (e.g., temperature, precipitation). This leaves significant gaps for an agent to understand how to use it effectively.
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 and front-loaded with the main purpose in the first sentence. The parameter explanations are clear and necessary. However, the structure could be slightly improved by integrating the parameter details more seamlessly, but it remains efficient with minimal waste.
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 that there is an output schema (which handles return values), the description doesn't need to explain outputs. However, with no annotations and a simple tool (2 parameters, no nested objects), the description covers the basics but lacks depth in behavioral aspects and usage context, making it adequate but with clear gaps.
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 description adds meaningful context beyond the input schema. The schema has 0% description coverage, only providing titles and types. The description explicitly lists and explains the parameters ('latitude: Latitude of the location', 'longitude: Longitude of the location'), compensating fully for the schema's lack of semantic information.
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 forecast for a location.' It specifies the verb ('Get') and resource ('weather forecast'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling tool 'get_alerts', which might also be weather-related.
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. It doesn't mention the sibling tool 'get_alerts' or any other context for choosing between them. Usage is implied by the purpose but lacks explicit when/when-not instructions.
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. Dates show when Glama detected each change.
v0.1.0get_alertsget_forecastThe two tools have clearly distinct purposes: get_alerts retrieves weather alerts for US states, while get_forecast provides forecasts for geographic coordinates. There is no overlap in functionality or ambiguity between them.
Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with the same verb 'get' and descriptive nouns. This makes the tool set predictable and easy to understand.
With only two tools, the server feels thin for a weather domain. It lacks basic operations like current conditions, historical data, or location search, making it incomplete for typical weather-related tasks.
The tool set is severely incomplete for a weather server. It misses core functionalities such as current weather, radar data, air quality, or unit conversions, leaving significant gaps that will hinder agent workflows.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Provide real-time and forecast weather information for locations in the United States using natura…
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
Smarter Weather MCP: forecasts, alerts, outlooks, observations, AQI, grids, and map imagery.
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/muminfarooq190/Weather'
If you have feedback or need assistance with the MCP directory API, please join our Discord server