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Tavily Web Search MCP Server

by inesaranab
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Python
Remote

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 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.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.git

After that, you can follow from Step 2. below!

Installation

  1. Clone the repository:

    git clone <repository-url>
cd <repository-directory>

  2. Configure environment variables: Copy the .env.sample to .env and add your Tavily API key:

    TAVILY_API_KEY=your_api_key_here

  3. 🏗️ 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 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.

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.

You can find details here!

Available Tools

3 tools
roll_diceC

Roll the dice with the given notation

ParametersJSON Schema
NameRequiredDescriptionDefault
notationYes
num_rollsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2/5.0
Behavior1/5

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.

Conciseness3/5

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.

Completeness2/5

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.

Parameters1/5

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.

Purpose3/5

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.

Usage Guidelines2/5

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.

text_to_speechC

Convert the given text into speech and save as MP3 file

ParametersJSON Schema
NameRequiredDescriptionDefault
textYes
voice_idNoJBFqnCBsd6RMkjVDRZzb

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.9/5.0
Behavior2/5

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 mentions converting text to speech and saving as MP3, implying a write operation, but lacks details on permissions, rate limits, file storage location, or error handling. This is a significant gap for a tool that likely involves file creation and external processing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste, front-loading the core functionality. It's appropriately sized for the tool's complexity, making it easy to parse without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (which likely describes the MP3 file or result), the description doesn't need to explain return values. However, with no annotations, 0% schema coverage, and two parameters, it's incomplete—missing behavioral context and parameter details. It's minimally adequate but has clear gaps in guiding usage and transparency.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 undocumented parameters. It mentions 'given text' and 'save as MP3 file', which loosely relates to the 'text' parameter but doesn't explain the 'voice_id' parameter or its default value. The description adds minimal meaning beyond the schema, failing to fully address the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 specific verbs ('convert', 'save') and resources ('text', 'MP3 file'), making it easy to understand what it does. However, it doesn't differentiate from sibling tools (roll_dice, web_search), which are unrelated, so it doesn't need sibling differentiation but could mention uniqueness in a broader context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 or in what contexts. It states what it does but offers no usage instructions, prerequisites, or exclusions, leaving the agent to infer appropriate scenarios without explicit help.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv1.0.0
    • Changedroll_dice1 field changed
      • addedInput schema / title
        Added value: +"roll_diceArguments"
    • Changedtext_to_speech1 field changed
      • addedInput schema / title
        Added value: +"text_to_speechArguments"
    • Changedweb_search1 field changed
      • addedInput schema / title
        Added value: +"web_searchArguments"
  2. 3 tool updates
    • First observedroll_dice
    • First observedtext_to_speech
    • First observedweb_search
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.8/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: rolling dice, converting text to speech, and searching the web. The descriptions are straightforward, making it impossible to confuse one tool for another.

Naming Consistency2/5

The naming is inconsistent with mixed conventions: 'roll_dice' and 'web_search' use snake_case, while 'text_to_speech' also uses snake_case but has a different verb style ('text_to' vs. 'roll'/'web'). There is no predictable pattern across the set.

Tool Count2/5

With only 3 tools, the set feels thin for a web search server, as the domain suggests a focus on search-related operations. The inclusion of dice-rolling and text-to-speech tools seems out of scope, making the count inappropriate for the apparent purpose.

Completeness2/5

For a web search server, there are significant gaps in the tool surface: it lacks core operations like filtering search results, getting detailed page content, or managing search history. The unrelated tools (dice, text-to-speech) do not contribute to domain coverage, leaving the search functionality incomplete.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • Tavily MCP Server

    RamXX
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    Provides AI-powered web search capabilities using Tavily's search API, enabling LLMs to perform sophisticated web searches, get direct answers to questions, and search recent news articles.
    72
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  • Tavily Web Search MCP Server

    debanshd
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    Enables web search capabilities through the Tavily API via the Model Context Protocol. Allows users to perform web searches and retrieve information from the internet through natural language queries.
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  • Tavily Web Search MCP Server

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    Enables web search capabilities through the Tavily API. Allows users to search the web for information using natural language queries through the MCP protocol.
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