The AI Makerspace MCP Server provides three tools via the Model Context Protocol (MCP):
Web Search (web_search): Search the web on any topic using the Tavily API by providing a query string.
Roll Dice (roll_dice): Roll dice using standard notation (e.g., 2d20k1 for 2 twenty-sided dice, keep highest 1), with an optional parameter for number of rolls.
Get Crypto Price (get_crypto_price): Retrieve current price and market data for a cryptocurrency (e.g., bitcoin, ethereum, dogecoin) from the CoinGecko API, with an optional comparison currency (default: usd).
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., "@AI Makerspace MCP Server search the web for the latest news on AI agents"
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: mcp-toolkit
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:
Create a .env file in the project root and add your API keys:
TAVILY_API_KEY=your_api_key_here
COINGECKO_API_KEY=your_coingecko_api_key_here # Optional - free tier works without key but has rate limitsNote: CoinGecko API can work without an API key, but using one provides higher rate limits (10-50 calls/minute with free tier).
🏗️ 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 several tools:
web_search: Search the web for information about a given query using Tavily API
roll_dice: Roll dice with standard notation (e.g., "2d20k1" for 2 twenty-sided dice, keep highest 1)
get_crypto_price: Get current cryptocurrency price and market data from CoinGecko API (e.g., "bitcoin", "ethereum")
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!
Get the current price and market data for a cryptocurrency.
Args: coin_id: The cryptocurrency ID (e.g., 'bitcoin', 'ethereum', 'dogecoin') vs_currency: The currency to compare against (default: 'usd')
Returns: Formatted string with current price and market data
| Name | Required | Description | Default |
|---|---|---|---|
| coin_id | Yes | ||
| vs_currency | No | usd |
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 returns a 'Formatted string with current price and market data,' which gives some output context, but lacks details on error handling, rate limits, authentication needs, or data freshness. 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 well-structured and appropriately sized, with a clear purpose statement followed by Args and Returns sections. Every sentence adds value, and it's front-loaded with the core functionality. Minor room for improvement in brevity, but it's efficient overall.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers purpose and parameters well but lacks behavioral details like error cases or rate limits. Without annotations or output schema, it should do more to guide the agent, making it adequate but not fully comprehensive.
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. It effectively explains both parameters: 'coin_id' as 'The cryptocurrency ID (e.g., 'bitcoin', 'ethereum', 'dogecoin')' and 'vs_currency' as 'The currency to compare against (default: 'usd')'. This adds clear meaning beyond the bare schema, covering semantics and examples, though it could note parameter constraints like valid currency 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 the current price and market data for a cryptocurrency.' It specifies the verb ('Get') and resource ('current price and market data for a cryptocurrency'), making it easy to understand. However, it doesn't differentiate from sibling tools like 'roll_dice' or 'web_search', which is unnecessary here since they serve completely different domains, so a 4 is appropriate.
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 any context-specific scenarios, prerequisites, or exclusions. While the sibling tools are unrelated, there's no explicit or implied usage advice, leaving the agent without operational context.
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 provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('Roll the dice') but doesn't describe what happens during execution, such as whether it's deterministic, random, or has side effects like logging. It also doesn't cover output format, error handling, or any constraints like rate limits. The description adds minimal behavioral context beyond the basic action.
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, efficient sentence that directly states the tool's action. It's front-loaded with the main purpose and avoids unnecessary words. However, it could be more structured by including key details, but as is, it's appropriately concise for its limited content.
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 has 2 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain the dice rolling behavior, parameter meanings, or what to expect as a result. For a tool that likely involves randomness and specific input formats, more context is needed to guide the agent effectively.
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 for the lack of parameter documentation. It mentions 'notation' but doesn't explain what it is (e.g., dice notation like '2d6'), and it doesn't address 'num_rolls' at all. The description adds little meaning beyond what the schema's titles provide, failing to clarify parameter usage or examples.
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 the action ('Roll the dice') and mentions the input ('with the given notation'), which gives a vague purpose. However, it doesn't specify what 'notation' means or what kind of dice rolling this involves (e.g., standard dice, RPG dice), and it doesn't differentiate from siblings like 'get_crypto_price' or 'web_search', which are unrelated tools. The purpose is understandable but lacks specificity.
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 any context, prerequisites, or exclusions, such as when dice rolling is appropriate compared to other tools on the server. Without any usage instructions, the agent must infer based on the tool 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 states the tool searches the web but doesn't disclose any behavioral traits such as rate limits, authentication needs, result formats, pagination, or potential side effects. This leaves significant gaps for an AI agent to understand how the tool behaves beyond its basic function.
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, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly. Every part of the sentence contributes to understanding the tool's function.
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 of a web search tool with no annotations, no output schema, and low parameter documentation (0% schema coverage), the description is incomplete. It lacks details on behavioral aspects, result handling, and parameter usage, which are crucial for effective tool invocation by an AI agent in this context.
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 minimal meaning beyond the input schema. It mentions 'the given query', which aligns with the single 'query' parameter in the schema, but schema description coverage is 0%, so the schema provides no details about the parameter. The description doesn't compensate by explaining query syntax, length limits, or examples, leaving the parameter semantics largely undocumented.
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 with a specific verb ('Search') and resource ('the web'), and specifies the action is about 'information about the given query'. It's not tautological and distinguishes itself from siblings like get_crypto_price and roll_dice by focusing on general web search rather than specific data retrieval or random generation.
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 any context, exclusions, or comparisons with sibling tools like get_crypto_price for cryptocurrency data or roll_dice for random number generation. Usage is implied only by the general purpose stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: get_crypto_price retrieves cryptocurrency market data, roll_dice performs a random dice roll, and web_search conducts web queries. There is no overlap in functionality, making tool selection straightforward for an agent.
The naming is mixed: get_crypto_price and web_search follow a verb_noun pattern, but roll_dice uses a verb-only format. While all names are readable, the inconsistency in structure (two with nouns, one without) reduces predictability across the set.
With only 3 tools, the server feels thin for its implied scope as an 'AI Makerspace' server, which suggests a broader utility or creative toolkit. The tools cover unrelated domains (crypto, dice, web search), making the set appear incomplete or poorly scoped rather than focused.
The server lacks a coherent domain, making completeness hard to assess, but there are significant gaps: for crypto, there's no historical data or portfolio tools; for dice, no customization options; for web search, no filtering or advanced parameters. The tools are isolated without supporting operations, leading to potential agent dead ends.
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