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., "@AIE8-MCP Server Search for the latest news on AI agents and the current weather in San Francisco"
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.
This project is a demonstration of the MCP (Model Context Protocol) server, which utilizes the Tavily API for web search capabilities. The server is designed to run in a standard input/output (stdio) transport mode.
Related MCP server: Python Weather MCP Server
The MCP server is set up to handle web search queries using the Tavily API. It is built with the following key components:
TavilyClient: A client for interacting with the Tavily API to perform web searches.
Python 3.13 or higher
A valid Tavily API key
You'll need to install this on the Windows side of your OS.
This will require getting two CLI tool for Powershell, which you can do as follows:
winget install astral-sh.uv
winget install --id Git.Git -e --source winget
After you have those CLI tools, please open Cursor into Windows.
Then, you can clone the repository using the following command in your Cursor terminal:
git clone https://AI-Maker-Space/AIE8-MCP-Session.gitAfter that, you can follow from Step 2. below!
Clone the repository:
git clone <repository-url>
cd <repository-directory>Configure environment variables:
Copy the .env.sample to .env and add your Tavily API key:
TAVILY_API_KEY=your_tavily_api_key_here
WEATHER_API_KEY=your_weather_api_key_here
OPENAI_API_KEY=your_openai_api_key_hereTo get a WeatherAPI key:
Sign up for a free account (provides 1 million calls/month)
Get your API key from the dashboard
🏗️ Add a new tool to your MCP Server 🏗️
Create a new tool in the server.py file, that's it!
To start the MCP server, you will need to add the following to your MCP Profile in Cursor:
NOTE: To get to your MCP config. you can use the Command Pallete (CMD/CTRL+SHIFT+P) and select "View: Open MCP Settings" and replace the contents with the JSON blob below.
{
"mcpServers": {
"mcp-server": {
"command" : "uv",
"args" : ["--directory", "/PATH/TO/REPOSITORY", "run", "server.py"]
}
}
}The server will start and listen for commands via standard input/output.
The server provides a web_search tool that can be used to search the web for information about a given query. This is achieved by calling the web_search function with the desired query string.
There are a few activities for this assignment!
Choose an API that you enjoy using - and build an MCP server for it!
Build a simple LangGraph application that interacts with your MCP Server.
You can find details here!
To run the LangGraph application that uses your MCP server:
python3 langgraph_app.pyOr try the demo version to see all MCP tools in action:
python3 demo_langgraph.pyThe application provides an interactive command-line interface where you can:
Ask about weather: "What's the weather in Seattle?"
Search the web: "Search for information about Python"
Roll dice: "Roll 2d20k1" or "Roll a die"
The app intelligently routes your requests to the appropriate MCP tools and provides responses using the LLM when needed.
What's Included:
langgraph_app.py - Full interactive LangGraph application with LLM integration
demo_langgraph.py - Quick demo showing all MCP tools working together
Get the weather for the given location
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden, but it only says 'Get the weather' without disclosing the data source, return format, units, caching, or any constraints. This is insufficient for the agent to anticipate tool behavior.
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 a single sentence, concise and front-loaded, but it is too minimal; it earns its place by specifying the input role but lacks detail that would justify its brevity.
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 no output schema, the description should hint at what is returned (e.g., temperature, conditions). It does not, leaving the agent uncertain about the tool's output. For a simple tool, this is inadequate.
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 one parameter 'city' has no description in the input schema (0% coverage), and the description merely says 'given location' which adds little meaning. It does not specify format, examples, or constraints on what constitutes a valid city.
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 verb 'Get' and resource 'weather' for a given location, distinguishing it from siblings 'roll_dice' and 'web_search' which serve different purposes.
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?
No guidance is given on when to use this tool versus alternatives. The description only states what the tool does, without any context on appropriate usage or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Roll the dice with the given notation
| Name | Required | Description | Default |
|---|---|---|---|
| notation | Yes | ||
| num_rolls | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits. It only says 'Roll the dice,' which is a direct restatement of the tool name and adds no depth about random generation, validation, error handling, or return format. This is effectively a tautology with no added 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 a single concise sentence with no waste, which is positive. However, it is under-specified: it front-loads little useful information and does not structure any context for the parameters. It cannot be considered 'appropriately sized' because it omits essential detail.
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?
For a tool with two parameters, no output schema, and no annotations, the description is severely inadequate. It does not explain dice notation, the meaning of num_rolls, or any behaviors/limitations. Even the sibling context (web_search) offers no help in situating this tool, making the description insufficient for reliable tool invocation.
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?
Schema description coverage is 0%, so the description must compensate. 'Given notation' loosely aligns with the 'notation' parameter but fails to define its format or semantics. The 'num_rolls' parameter is entirely absent from the description, leaving its purpose unexplained. Overall, minimal added meaning beyond the schema titles.
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 states a clear verb ('Roll') and resource ('dice'), indicating the tool's primary action. However, it does not specify what 'notation' means (e.g., standard dice notation like '2d6'), which leaves some ambiguity. It is distinct from the sibling web_search, but not explicitly differentiated.
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?
No guidance is provided on when to use this tool vs alternatives. The sibling list includes web_search, but the description gives no context for when dice rolling is appropriate or any exclusions. Users are left to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the web for information about the given query
| Name | Required | Description | Default |
|---|---|---|---|
| query | 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 only says 'search the web' without explaining what the tool returns, any limitations (e.g., freshness, pagination), or side effects. This lack of detail leaves the agent uncertain about the tool's behavior beyond the obvious.
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 a single sentence, ten words long, and front-loaded with the core action. There is no wasted text or redundancy. It is appropriately concise for a simple tool, and every word earns its place.
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?
The tool is low-complexity with one parameter and no output schema. The description fails to explain the return value, result format, or any constraints/edge cases. Since there is no output schema, the description should cover what the agent can expect after invocation, but it does not. This makes the description incomplete for an agent to use correctly.
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 has one parameter 'query' with a string type, and the description mentions 'the given query,' which is redundant. Since schema description coverage is 0%, the description should add meaning about the query format, examples, or constraints, but it does not. The parameter is self-explanatory, but the description adds no extra value.
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 function: 'Search the web for information about the given query.' It uses a specific verb ('search') and resource ('web'), and the query is self-evident. While it doesn't explicitly distinguish from siblings, the only sibling is 'roll_dice,' which is clearly unrelated, so no differentiation is needed.
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 implies usage: use this tool when you need web information. However, it provides no explicit guidance on when to prefer this tool over alternatives, when not to use it, or any prerequisites. There is no mention of exclusions or comparison with the sibling tool, so it remains at an implied level.
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.
v0.1.0get_weatherroll_diceweb_searchScored across 3 tools
Each tool has a completely distinct purpose (weather, dice rolling, web search) with no overlap, making it easy for an agent to select the correct one.
All tool names follow the consistent verb_noun pattern (get_weather, roll_dice, web_search) without any mixing of conventions.
With 3 tools, the count is appropriate for a focused utility server; each tool is distinct and earns its place.
The tool set lacks a coherent domain—it's a random collection of unrelated functions (weather, dice, search) with obvious gaps for a general-purpose assistant (e.g., no calendar, math, or storage tools).
Hosted MCP server for live public-data APIs and Skills for AI agents.
Docs: https://docs.keenable.ai/mcp-server Keenable is a free, remote MCP server that gives agents access to the web index. Search the web with ranked results and date/site filters, then fetch any indexed page as clean markdown. Works out of the box with no account or API key.
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.