MCP Server for Session 13
Allows fetching marketing and business news articles related to HubSpot.
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., "@MCP Server for Session 13get the latest news about ZoomInfo"
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.
AI Makerspace: MCP Session Repo for Session 13
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
Project Overview
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.
Prerequisites
Python 3.13 or higher
A valid Tavily API key
⚠️NOTE FOR WINDOWS:⚠️
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.uvwinget 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!
Installation
Clone the repository:
git clone <repository-url> cd <repository-directory>Configure environment variables: Create a
.envfile and add your API keys:TAVILY_API_KEY=your_tavily_api_key_here NEWS_API_KEY=your_news_api_key_hereGet your Tavily API key at: https://tavily.com/
Get your free NewsAPI key at: https://newsapi.org/register
🏗️ Add a new tool to your MCP Server 🏗️
Create a new tool in the server.py file, that's it!
Running the MCP Server
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.
Usage
The server provides several tools for different functionalities:
Available Tools:
web_search(query: str)- Search the web for information about a given query using Tavily APIroll_dice(notation: str, num_rolls: int)- Roll dice with custom notation (e.g., "2d6", "1d20")get_marketing_news(company: str, category: str, num_articles: int)- Get latest marketing and business news from companies like ZoomInfo, 6sense, HubSpot, etc.get_company_news(company: str, num_articles: int)- Get specific news about a particular company
Example Usage:
get_marketing_news()- Get general marketing tech newsget_marketing_news(company="ZoomInfo")- Get ZoomInfo-specific newsget_company_news(company="6sense")- Get 6sense company newsroll_dice("2d6", 3)- Roll 2 six-sided dice 3 times
Activities:
There are a few activities for this assignment!
🏗️ Activity #1:
Choose an API that you enjoy using - and build an MCP server for it!
🏗️ Activity #2:
Build a simple LangGraph application that interacts with your MCP Server.
Files for Activity #2:
langgraph_app.py- LangGraph application with MCP integration
To test Activity #2:
uv run langgraph_app.pyAvailable Tools
4 toolsget_company_newsA
Get specific news about a particular company (e.g., ZoomInfo, 6sense, etc.)
Args: company: Company name to search for num_articles: Number of articles to return (default: 3)
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | ||
| num_articles | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a read operation (get news) with no destructive hints. However, without annotations, it does not disclose details like rate limits, pagination, or source of news, leaving some behavioral ambiguity.
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 short and mostly clear, though the Args block duplicates schema information. The core purpose sentence is well-placed. Minor redundancy but no unnecessary verbosity.
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 simple tool with 2 parameters and no output schema, the description covers the basic function. It is missing output format details (e.g., headlines vs. full articles) and potential constraints, but is minimally adequate.
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 coverage, the description adds semantic value by explaining each parameter: 'Company name to search for' and 'Number of articles to return (default: 3)'. This compensates for the bare schema (which only has 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 clearly states the tool gets company-specific news, with examples (e.g., ZoomInfo, 6sense). It distinguishes from siblings like 'get_marketing_news' (broader marketing news) and 'web_search' (general web results).
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 guidance on when to use this tool versus alternatives. The description implies it is for company-specific news, but lacks when-not-to-use criteria or references to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_marketing_newsB
Get the latest marketing and business news from companies like ZoomInfo, 6sense, and other marketing tech companies.
Args: company: Specific company to search for (e.g., 'ZoomInfo', '6sense', 'HubSpot', 'Salesforce') category: News category (default: 'business', options: 'business', 'technology', 'general') num_articles: Number of articles to return (default: 5, max: 100)
| Name | Required | Description | Default |
|---|---|---|---|
| company | No | ||
| category | No | business | |
| num_articles | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It does not disclose if the tool is read-only, has side effects, requires authentication, or any rate limits. Only describes basic functionality.
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?
Concise paragraph followed by a structured Args list. Front-loaded with purpose. Could trim minor redundancy but overall efficient.
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?
Adequately covers parameters and purpose given no annotations or output schema. Lacks guidance on use cases, return format, or limitations, but is sufficient for basic usage.
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's Args section explains each parameter (company, category, num_articles) with examples and defaults, adding meaning beyond the schema's type and default values.
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 retrieves the latest marketing and business news from specific companies like ZoomInfo and 6sense. The verb 'Get' and resource 'marketing and business news' are specific, but lacks explicit differentiation from sibling tool 'get_company_news'.
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?
Provides examples of companies and categories but no guidance on when to use this tool versus alternatives like 'get_company_news'. No when-not-to-use or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
roll_diceC
Roll the dice with the given notation
| Name | Required | Description | Default |
|---|---|---|---|
| notation | Yes | ||
| num_rolls | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description does not disclose randomness, side effects, or return format. For a simple tool, more transparency is needed.
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?
Single sentence is concise but lacks necessary detail for a 2-parameter tool. Front-loads the action but omits important context.
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?
No output schema; description does not explain return values. Minimal details for notation and num_rolls leave the tool incomplete.
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%. Description fails to explain parameter meaning, e.g., notation format or num_rolls behavior, leaving critical gaps.
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 'Roll the dice with the given notation' clearly states the action (roll) and resource (dice), implying dice-rolling. It distinguishes from unrelated siblings (news, web search).
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 on when to use this tool or its alternatives. Siblings are unrelated, but no usage context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
web_searchC
Search the web for information about the given query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description lacks any disclosure of behavioral traits such as rate limits, pagination, or result format.
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?
Single sentence with no waste, but overly minimal without any structure or additional context.
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 simple one-parameter tool, the description is incomplete; lacks details on return values, safety, or usage scenarios.
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 beyond restating the parameter; does not explain how to formulate the query or any constraints.
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 'search' and resource 'web', and distinguishes from sibling tools like get_company_news and get_marketing_news which are more specific.
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 on when to use this tool versus alternatives, no exclusions or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
get_company_news and get_marketing_news have significant overlap, both retrieving news about marketing tech companies, making it unclear which to use. The other tools (roll_dice, web_search) are unrelated, further complicating disambiguation.
Two tools follow a 'get_X_news' pattern, but roll_dice and web_search break that pattern, using different verb styles. The naming is inconsistent across the set.
With only 4 tools spanning news, dice rolling, and web search, the count feels arbitrary and too small for a coherent domain. The server appears to bundle unrelated utilities.
The coverage is severely incomplete for any clear purpose. If focused on marketing news, dice rolling and web search are extraneous; if general, many common operations are missing. The set lacks a coherent domain.
Resources
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