A
licenseA
qualityB
maintenanceA Model Context Protocol server that provides real-time web search capabilities to AI assistants through pluggable search providers, currently integrated with the Brave Search API.
5
16
MIT
Brave Search MCP Server
by arben-adm
The Brave Search MCP Server integrates Brave Search capabilities into AI assistants via the Model Context Protocol (MCP), enabling two main functions:
Web Searches: Use the
brave_web_searchtool to execute web searches with the Brave Search API, specifying search terms and result count (default: 20).Local Business Searches: Use the
brave_local_searchtool to find location-based businesses and places, with customizable result count.
Allows searching the web and for local businesses using the Brave Search API
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., "@Brave Search MCP Serverfind recent articles about quantum computing advancements"
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.
Brave Search MCP Server
This project implements a Model Context Protocol (MCP) server for Brave Search, allowing integration with AI assistants like Claude.
Prerequisites
Python 3.11+
uv - A fast Python package installer and resolver
Related MCP server: Brave Search MCP Server
Installation
Installing via Smithery
To install Brave Search MCP server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @arben-adm/brave-mcp-search --client claudeManual Installation
Clone the repository:
git clone https://github.com/your-username/brave-search-mcp.git cd brave-search-mcpCreate a virtual environment and install dependencies using uv:
uv venv source .venv/bin/activate # On Windows, use: .venv\Scripts\activate uv pip install -r requirements.txtSet up your Brave Search API key:
export BRAVE_API_KEY=your_api_key_hereOn Windows, use:
set BRAVE_API_KEY=your_api_key_here
Usage
Configure your MCP settings file (e.g.,
claude_desktop_config.json) to include the Brave Search MCP server:{ "mcpServers": { "brave-search": { "command": "uv", "args": [ "--directory", "path-to\\mcp-python\\brave-mcp-search\\src", "run", "server.py" ], "env": { "BRAVE_API_KEY": "YOUR_BRAVE_API_KEY_HERE" } } } }Replace
YOUR_BRAVE_API_KEY_HEREwith your actual Brave API key.Start the Brave Search MCP server by running your MCP-compatible AI assistant with the updated configuration.
The server will now be running and ready to accept requests from MCP clients.
You can now use the Brave Search functionality in your MCP-compatible AI assistant (like Claude) by invoking the available tools.
Available Tools
The server provides two main tools:
brave_web_search: Performs a web search using the Brave Search API.brave_local_search: Searches for local businesses and places.
Refer to the tool docstrings in src/server.py for detailed usage information.
Development
To make changes to the project:
Modify the code in the
srcdirectory as needed.Update the
requirements.txtfile if you add or remove dependencies:uv pip freeze > requirements.txtRestart the server to apply changes.
Troubleshooting
If you encounter any issues:
Ensure your Brave API key is correctly set.
Check that all dependencies are installed.
Verify that you're using a compatible Python version.
If you make changes to the code, make sure to restart the server.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Available Tools
2 toolsbrave_local_searchC
Search for local businesses and places
Args:
query: Location terms
count: Results (1-20
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| count | No |
TDQS
C2.7/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Search' implies a read-only operation, it doesn't specify authentication requirements, rate limits, data sources, or what happens when no results are found. The description provides minimal behavioral context beyond the basic operation.
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 appropriately concise with a clear purpose statement followed by parameter explanations. The formatting with 'Args:' section is helpful, though the incomplete 'Results (1-20' text suggests a typographical error that slightly detracts from professionalism.
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?
For a search tool with 2 parameters, 0% schema description coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain what information is returned, result format, error conditions, or how this differs from the sibling web search tool, leaving significant gaps for an AI agent.
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. It provides basic explanations for both parameters ('Location terms' for query, 'Results (1-20' for count), but these are minimal and don't explain format expectations, what constitutes valid 'Location terms', or how the count parameter works with the default value of 20.
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 as 'Search for local businesses and places' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from its sibling 'brave_web_search' which likely searches the broader web rather than local businesses.
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 its sibling 'brave_web_search' or any alternatives. There's no mention of appropriate contexts, prerequisites, or exclusions for using this local search functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
brave_web_searchC
Execute web search using Brave Search API with improved results
Args:
query: Search terms
count: Desired number of results (10-20)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| count | No |
TDQS
C2.9/5.0
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 behavioral disclosure. It mentions 'improved results' but doesn't explain what this means (e.g., better ranking, additional metadata). It lacks details on rate limits, authentication needs, error handling, or what the output looks like (e.g., format of search results). For a tool with no annotation coverage, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. The two-sentence structure is efficient, with the second sentence dedicated to parameter details. There's minimal waste, though the formatting with indentation and quotes might slightly affect readability in some contexts.
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?
Given the complexity (a web search tool with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return values (e.g., what 'improved results' include), error conditions, or usage constraints. For a tool that interacts with an external API, more context is needed to ensure reliable agent invocation.
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?
Schema description coverage is 0%, so the description must compensate. It adds basic semantics for both parameters: 'query' as 'Search terms' and 'count' as 'Desired number of results (10-20)'. This clarifies the purpose of each parameter beyond the schema's titles ('Query' and 'Count'). However, it doesn't provide deeper context like query syntax examples or why count has a range, leaving room for improvement.
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: 'Execute web search using Brave Search API with improved results.' It specifies the verb ('Execute web search') and resource ('Brave Search API'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling 'brave_local_search' (e.g., by mentioning this is for general web searches vs. local searches), which prevents a perfect score.
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. It doesn't mention the sibling tool 'brave_local_search' or any other potential alternatives, nor does it specify contexts or prerequisites for usage. The only implied usage is for web searches, but this is too vague for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
B3.2/5.0
Disambiguation5/5
The two tools have clearly distinct purposes: one is for local business/place searches and the other is for general web searches. The descriptions explicitly differentiate their domains (local vs. web), making it impossible to confuse them. Each tool serves a unique function within the search domain.
Naming Consistency5/5
Both tools follow a perfectly consistent naming pattern: 'brave_' prefix followed by descriptive snake_case (local_search, web_search). The naming convention is uniform across all tools, making them predictable and easy to understand. There are no deviations or mixed styles.
Tool Count3/5
With only 2 tools, the server feels somewhat thin for a search domain that could benefit from more specialized operations (e.g., image search, news search, autocomplete). While the two core search functions are covered, the limited tool count may restrict agent capabilities for broader search-related tasks. It's borderline minimal but functional.
Completeness4/5
The server covers the essential search operations (local and web) well, but there are minor gaps in the search surface. For instance, no tools for image search, news search, or search suggestions/autocomplete are included, which are common in search APIs. However, the core workflows are adequately supported, and agents can work around these omissions.
Maintenance
ActivityInactive
ResponsivenessSyncing
Resources
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Looking for Admin?
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Related MCP Connectors
Visit https://brave.com/search/api/ for a free API key. Search the web, local businesses, images,…
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Brave Search MCP — independent web index (no Google/Bing dependency)
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