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., "@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, and the description does not disclose behavioral traits (e.g., randomness, side effects, idempotency). The tool's safety profile is completely unaddressed.
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 with no wasted words. It is front-loaded with the action. However, it lacks necessary detail, so highest score is not warranted.
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 simple, but the description fails to explain the notation format, default values, or return values. Without this, an AI agent cannot correctly invoke the tool.
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%, and the description adds no meaning to the parameters. 'Notation' is not defined (e.g., expected format like '2d6'), and 'num_rolls' is not mentioned.
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 action (roll) and resource (dice), and the tool name aligns. It distinguishes from siblings (weather, search). However, 'notation' is vague without further explanation.
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 versus alternatives. There is no mention of context, prerequisites, or exclusions.
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 present, so the description carries the full burden. It does not disclose any behavioral traits such as rate limits, read-only nature, or what the search returns. The tool is assumed to be a read operation but this is unstated.
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 unnecessary words. It is appropriately short for a simple tool with one parameter.
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 simplicity (single parameter, no output schema), the description provides the bare minimum. It does not explain what the tool returns (e.g., search results), which is needed for an agent to use it effectively. Lacks completeness.
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%, but the description adds minimal value beyond the schema. It explains the query is for web search but provides no formatting guidance, length limits, or examples. The single parameter 'query' is not further detailed.
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 action ('search') and the resource ('the web'), making the purpose immediately obvious. It distinguishes from sibling tools (get_weather, roll_dice) which do different things.
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 versus others, or any context about appropriate queries. The description lacks any 'when to use' or 'when not to use' information.
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_weatherroll_diceweb_searchEach 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).
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/keertanachandar/AIE8-MCP'
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