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., "@AIE7-hw13-mcp search for recent advancements in quantum computing"
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: Tavily Web Search 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/AIE7-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_api_key_here🏗️ 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!
Please check findicon.py & server.py.
Build a simple LangGraph application that interacts with your MCP Server.
You can find details here!
Please check langchainagentmcp.py
Download icons for the given query to local directory
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | ||
| count | No | ||
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states that icons are downloaded to a local directory, but does not clarify important behaviors such as whether files are overwritten, whether a directory is created if missing, what file naming conventions are used, or what the tool returns upon success or failure. For a write side-effect tool, this is insufficient.
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 clear sentence with no redundant words, making it concise and front-loaded. It could be slightly more informative without becoming bloated, but it does not waste words.
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 and no annotations, the description should explain the full context of the tool's behavior, including return values, error conditions, and parameter effects. The provided one-liner does not cover these, leaving significant gaps for the agent to infer.
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 for explaining parameters. It only implicitly references 'query' via the phrase 'given query', but leaves 'size' and 'count' completely undefined. The tool does not provide any guidance on how these affect the download, forcing the agent to guess.
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: 'Download icons for the given query to local directory'. It specifies the action (download), the object (icons), the source (query), and the destination (local directory). This distinguishes it from sibling tools like find_icon and show_icon_info, 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?
The description provides clear context for when to use this tool: when you need to download icons based on a query to a local directory. However, it does not explicitly mention when not to use it or name alternative tools like find_icon for searching, so it falls short of the highest score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find an icon for the given query
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| query | 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 merely states the action without detailing what the function returns, whether it can return multiple results, whether it accesses external resources, or any side effects. This is insufficient for an agent to predict 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, concise sentence that front-loads the main action. It wastes no words and is easy to parse, though its brevity comes at the cost of completeness.
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 no annotations, no output schema, and minimal parameter descriptions, the overall context is incomplete. The agent has no information about return format, pagination, icon sources, or interpretation of results. This is inadequate for reliable tool selection and 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?
The description mentions 'given query' which maps to the required 'query' parameter, but it does not explain the 'count' parameter at all. With 0% schema description coverage, the description needed to clarify all parameters, but it only covers half of them and even then vaguely.
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 a specific verb ('Find') and resource ('icon'), and the query parameter is mentioned. It identifies the tool's core function, though it does not distinguish it from siblings like download_icons or show_icon_info, which could also involve finding icons.
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 usage guidance is provided. The description does not indicate when to use find_icon versus alternatives like download_icons or web_search, nor does it specify any prerequisites or exclusions. The intended context is implied only by the tool's name.
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 |
| 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 fails to mention the format of accepted notation, whether the roll is random, any constraints, error behavior, or the return value. This is a significant transparency gap.
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 very short and front-loaded, but it is under-specified. Every sentence is technically earned but the content is insufficient. It is concise at the expense of usefulness.
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?
Despite being a simple tool, the description is incomplete for an agent to use it correctly. It does not explain the expected notation syntax or the number of rolls, and while an output schema exists, the description does not help connect inputs to outputs. The description needs more detail to be functionally complete.
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 for parameter meaning. It only vaguely references 'notation' and completely ignores the 'num_rolls' parameter. The description provides essentially no semantic information beyond the parameter names.
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 is essentially a restatement of the tool name ('roll dice') with a vague reference to 'given notation'. It communicates the basic action but adds no detail about what notation is or how it differs from other tools. It's minimally clear but not strongly 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?
There is no guidance on when to use this tool versus alternatives, nor any context about suitable scenarios. The description simply states the action without implying any usage conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show detailed information about found icons
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only states a generic action. It does not mention whether the operation is read-only, whether network calls or permissions are needed, or any side effects. The verb 'show' implies read-only but this is not explicitly stated.
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, clear sentence that is front-loaded and free of redundancy. However, it is under-specified, which slightly detracts from what would otherwise be a very concise entry.
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 two parameters, no annotations, and a workflow implied by siblings, the description is incomplete. It does not explain the relationship to find_icon, how query affects results, or what 'detailed information' includes. The output schema exists but does not cover usage 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?
Schema description coverage is 0%, and the description does not compensate. It fails to explain the purpose of 'query' or 'count', leaving the agent without information on how to fill these parameters correctly.
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 'Show detailed information about found icons' clearly specifies a verb (show) and resource (detailed information about found icons). It is specific enough to distinguish from siblings like download_icons or roll_dice, though it does not explicitly contrast with find_icon.
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 explicit usage guidance is provided. The phrase 'found icons' vaguely implies a prior step, but there is no mention of when to use this tool versus find_icon or other siblings, nor any 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 |
| 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 disclosing behavioral traits. It does not mention whether the tool is read-only, what kind of results it returns, any potential side effects, or limitations. The description simply restates the action without adding safety or behavioral context.
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, short sentence that is appropriately sized and front-loaded. It contains no fluff or redundant words, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool is simple and has an output schema, the description lacks essential contextual information such as when to use it, what the query parameter entails, and any behavioral details. Given the absence of annotations and minimal parameter guidance, the description is insufficient for fully correct tool invocation in ambiguous situations.
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?
With 0% schema description coverage and only one parameter, the description should explain what constitutes a valid query, any formatting requirements, or how the query is used. It only says 'about the given query,' which adds no meaningful detail beyond the parameter name and type.
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 performs a web search for the given query, using the specific verb 'search' and resource 'the web'. It implicitly distinguishes itself from sibling tools like github_search_repositories by specifying the web as the scope, but it does not explicitly name or contrast with alternatives.
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 offers no guidance on when to use this tool versus other sibling tools. It does not mention any preconditions, alternatives, or specific contexts, leaving the agent to infer usage only from the tool's name and generic purpose.
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.
v0.1.0download_iconsfind_iconroll_diceshow_icon_infoweb_searchScored across 5 tools
Each tool performs a distinct action: web search, dice rolling, icon search, icon download, and icon info display. The only potential overlap is between find_icon and download_icons, but their descriptions clarify searching vs. downloading. No two tools have the same purpose.
All tools follow a consistent verb_noun pattern with lowercase and underscores (web_search, roll_dice, find_icon, download_icons, show_icon_info). The convention is uniform across the set.
The server exposes 5 tools, which is within the ideal 3-15 range. While the tools cover unrelated domains, the count is appropriate for the apparent scope of a small utility server.
For the icon workflow, find, download, and info provide a functional cycle. Web search and dice rolling are each represented by a single tool, covering their core use case. There are no obvious dead ends, though the lack of a unified domain makes completeness harder to assess.
Serper MCP — wraps the Serper Google Search API (serper.dev)
MCP server for Google search results via SERP API
