How do I use Tavily Web Search MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Tavily Web Search MCP Server find recent developments 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.

Tavily Web Search MCP Server

by debanshd

Overview Schema Source Related Servers Discussions Admin

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 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.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.git

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

Installation

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!

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

5 tools

read_clipboardA

Read and return the current clipboard text (macOS). Uses pbpaste.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the tool reads clipboard text using `pbpaste` on macOS, which implies platform dependency and read-only behavior. However, it lacks details on error handling, permissions needed, or what happens if clipboard is empty, leaving behavioral gaps.

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 extremely concise with two sentences that efficiently convey purpose, platform, and implementation. Every word adds value, and it is front-loaded with the core action, making it highly efficient and well-structured.

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

Completeness4/5

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

Given the tool's simplicity (0 parameters, no annotations, but has an output schema), the description is mostly complete. It covers the action, resource, and platform, but could benefit from mentioning the output format or handling edge cases, though the output schema may mitigate this.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description does not add parameter semantics, but this is appropriate given the lack of parameters, warranting a baseline score above 3 for adequate handling.

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

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Read and return') and resource ('current clipboard text'), with platform specificity ('macOS') and implementation detail ('Uses `pbpaste`'). It distinguishes from sibling 'write_clipboard' by being the read counterpart.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'macOS' and the clipboard resource, but does not explicitly state when to use this tool versus alternatives like 'write_clipboard' or other data retrieval methods. It provides clear context but lacks explicit exclusions or alternatives.

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

ParametersJSON Schema

Name	Required	Description	Default
`notation`	Yes
`num_rolls`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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.

send_gmailC

Send an email using Gmail API. Requires GMAIL_USER and GMAIL_APP_PASSWORD environment variables.

ParametersJSON Schema

Name	Required	Description	Default
`to`	Yes
`subject`	Yes
`body`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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 authentication requirements (environment variables), which is useful context. However, it lacks details on critical behaviors such as rate limits, error handling, whether emails are sent immediately or queued, or any side effects (e.g., saving drafts). This leaves significant gaps for a mutation tool.

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

Conciseness4/5

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

The description is concise with two sentences that directly state the tool's purpose and prerequisites. It is front-loaded with the main action, and there is no unnecessary information, making it efficient. However, it could be slightly more structured by explicitly listing parameters or usage examples.

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's complexity (a mutation tool for sending emails), the absence of annotations, and the presence of an output schema (which may cover return values), the description is partially complete. It includes authentication details but misses behavioral traits and parameter semantics. For a tool with no annotations and 0% schema coverage, it should provide more context to be fully adequate.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the schema provides no parameter descriptions. The tool description does not mention any parameters (to, subject, body), offering no semantic information beyond what the bare schema titles imply. Since there are 3 parameters, this lack of detail results in a baseline score of 3, as the description fails to compensate for the low coverage.

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 action ('Send an email') and the resource ('using Gmail API'), providing a specific verb+resource combination. It distinguishes this tool from siblings like 'read_clipboard' or 'web_search' by focusing on email functionality, though it doesn't explicitly differentiate from non-existent email-related siblings.

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 mentions environment variable prerequisites ('Requires GMAIL_USER and GMAIL_APP_PASSWORD'), which provides some usage context. However, it offers no guidance on when to use this tool versus alternatives (e.g., other email-sending methods or tools), nor does it specify exclusions or typical scenarios for its application.

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

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.8/5.0

Behavior2/5

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 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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters2/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

write_clipboardA

Write the given text to the clipboard (macOS). Uses pbcopy.

ParametersJSON Schema

Name	Required	Description	Default
`text`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the tool writes text to the clipboard on macOS using `pbcopy`, which implies a mutation operation. However, it lacks details on permissions, error handling, or side effects, offering only basic 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.

Conciseness5/5

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

The description is front-loaded with the core action and resource, followed by implementation details. It consists of two concise sentences with no wasted words, making it highly efficient and easy to parse.

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

Completeness4/5

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

Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose, platform, and method, though it could benefit from more behavioral details like error cases or limitations.

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

Parameters4/5

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. It adds meaning by explaining that the 'text' parameter is the content to write to the clipboard, which clarifies the parameter's purpose beyond the schema's basic type definition. Since there's only one parameter, this is sufficient for a high score.

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

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Write the given text') and target resource ('to the clipboard'), with additional implementation detail ('macOS') and method ('Uses `pbcopy`'). It distinguishes itself from sibling tools like 'read_clipboard' by specifying the write operation.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'macOS', which suggests this tool is platform-specific. However, it does not explicitly state when to use it versus alternatives (e.g., other clipboard methods or tools) or provide exclusions, leaving some guidance gaps.

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.

5 tool updatesDec 10, 2025
- First observedread_clipboard
- First observedroll_dice
- First observedsend_gmail
- First observedweb_search
- First observedwrite_clipboard

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

C2.9/5.0

Scored 2026-04-18 09:01 across 5 tools

Disambiguation1/5

The tools have wildly different and unrelated purposes—clipboard operations, dice rolling, email sending, and web searching—with no clear thematic connection, making the set highly ambiguous as a cohesive toolset. An agent would struggle to understand why these specific tools are grouped together, leading to potential misselection based on domain assumptions.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (e.g., read_clipboard, roll_dice, send_gmail, web_search, write_clipboard), which is predictable and readable. There are minor deviations like 'web_search' using a noun_verb structure instead, but overall the consistency is strong.

Tool Count2/5

With only 5 tools, the count is reasonable in isolation, but it is inappropriate for the server's stated purpose as 'Tavily Web Search MCP Server'—only one tool (web_search) directly relates to web searching, while the others are unrelated utilities, creating a mismatch in scope.

Completeness2/5

For a web search server, there are significant gaps in coverage, such as missing tools for advanced search features, result filtering, or API configuration. The inclusion of unrelated tools like clipboard and email operations further dilutes the domain focus, making the surface incomplete for the stated purpose.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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AI Makerspace: MCP Session Repo for Session 13

Project Overview

Prerequisites

⚠️NOTE FOR WINDOWS:⚠️

Installation

Running the MCP Server

Usage

Activities:

🏗️ Activity #1:

🏗️ Activity #2:

Available Tools

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches