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licenseC
qualityD
maintenanceA starter template for building MCP servers that can integrate with Claude, Cursor, or other MCP-compatible AI assistants to create custom tools, resource providers, and prompt templates.
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12 npm
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MCP Server Example
The MCP Server Example allows you to retrieve weather-related information:
Get weather alerts for a specific U.S. state using its two-letter code
Get weather forecasts for a specific location using latitude and longitude coordinates
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., "@MCP Server Examplewhat's the weather forecast for Austin 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.
MCP Server Example
This project is a very basic MCP server ready to be consumed by Cursor or other tools.
Modify index.ts and add any tools you want, or keep the default ones.
Then build the project:
pnpm run buildAnd run it:
pnpm run startNow add a .cursor/mcp.json file in your project, and paste the following;
{
"mcpServers":{
"weather":{
"command": "node",
"args": ["[path]/build/index.js"]
}
}
}Replace [path] with the absolute path to this project. Run pwd in the project folder for that. For example, in my case it is:
{
"mcpServers":{
"weather":{
"command": "node",
"args": ["/Users/ivanbtrujillo/Development/mcp-server-spike/build/index.js"]
}
}
}Now go to "Settings" in cursor. You should be able to see the MCP server there. Enable it (click on the top right)
Now go to the chat, switch to Agent mode and select claude-3.7-sonnet as your model.
Type the following:
What is the weather in Texas?It will use the MCP to get first the weather alerts, and then the forecast Here is an example of how the chat interface looks when querying the weather in Texas:
Available Tools
2 toolsget-alertsC
Get weather alerts for a state
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code (e.g. CA, NY) |
TDQS
C2.9/5.0
Behavior2/5
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 what the tool does but doesn't describe traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 a single, clear sentence with no wasted words, making it highly concise and front-loaded. It efficiently communicates the core purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex alert data. It doesn't explain what the alerts include, how they're formatted, or any limitations, leaving the agent with insufficient context for effective use.
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 doesn't add any parameter-specific information beyond what's in the input schema, which has 100% coverage and fully documents the 'state' parameter. This meets the baseline score of 3, as the schema adequately handles parameter semantics without needing extra description.
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 verb ('Get') and resource ('weather alerts for a state'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling tool 'get-forecast', which likely provides different weather data, so it doesn't reach the highest score.
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 its sibling 'get-forecast' or any alternatives. It lacks context about usage scenarios, exclusions, or prerequisites, offering only a basic statement of function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-forecastC
Get weather forecast for a location
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude of the location | |
| longitude | Yes | Longitude of the location |
TDQS
C2.9/5.0
Behavior2/5
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 what the tool does but doesn't describe any behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, or what the response format might be. This is inadequate for a tool with zero annotation coverage.
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 a single, clear sentence that efficiently conveys the core purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations and no output schema, the description is incomplete. It doesn't provide enough context about behavioral aspects, response format, or how this tool differs from its sibling. For a tool with this level of complexity and lack of structured data, the description should do more to compensate.
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 schema description coverage is 100%, with both parameters (latitude and longitude) well-documented in the schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema, so it meets the baseline score of 3.
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 verb ('Get') and resource ('weather forecast for a location'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'get-alerts', which likely provides different weather-related information, so it doesn't achieve the highest score.
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 the sibling 'get-alerts' or explain the difference between getting a forecast versus alerts, leaving the agent without context for tool selection.
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.
2 tool updates
- First observed
get-alerts - First observed
get-forecast
TDQS
B3.1/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: one retrieves weather alerts for a state, and the other retrieves weather forecasts for a location. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the need for alerts versus forecasts.
Naming Consistency5/5
Both tools follow a consistent verb-noun pattern with hyphens (get-alerts, get-forecast), using the same verb 'get' and descriptive nouns. This predictable naming scheme enhances readability and reduces confusion for agents.
Tool Count2/5
With only two tools, the server feels thin for a weather domain that typically involves more operations, such as current conditions, historical data, or radar imagery. This limited set may not fully support comprehensive weather-related tasks.
Completeness2/5
The tool set is severely incomplete for a weather server, lacking essential operations like getting current conditions, historical weather data, or radar images. This creates significant gaps that could lead to agent failures in handling broader weather queries.
Maintenance
ActivityInactive
ResponsivenessNo issues
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