Exa Search Extension
This tutorial covers how to add the Exa MCP Server as a goose extension to enable AI-powered web search functionality.
Quick Install
- goose Desktop
- goose CLI
Command
npx -y exa-mcp-server
Environment Variable
EXA_API_KEY: <YOUR_API_KEY>
Configuration
info
Note that you'll need Node.js installed on your system to run this command, as it uses npx.
- goose Desktop
- goose CLI
- Launch the installer
- Click
Yesto confirm the installation - Get your Exa API Key and paste it in
- Click
Add Extension - Click the button in the top-left to open the sidebar
- Navigate to the chat
- Run the
configurecommand:
goose configure
- Choose to add a
Command-line Extension.
┌ goose-configure
│
◇ What would you like to configure?
│ Add Extension
│
◆ What type of extension would you like to add?
│ ○ Built-in Extension
│ ● Command-line Extension (Run a local command or script)
│ ○ Remote Extension (Streamable HTTP)
└
- Give your extension a name.
┌ goose-configure
│
◇ What would you like to configure?
│ Add Extension
│
◇ What type of extension would you like to add?
│ Command-line Extension
│
◆ What would you like to call this extension?
│ Exa Search
└
- Enter the command to run when this extension is used.
┌ goose-configure
│
◇ What would you like to configure?
│ Add Extension
│
◇ What type of extension would you like to add?
│ Command-line Extension
│
◇ What would you like to call this extension?
│ Exa Search
│
◆ What command should be run?
│ npx -y exa-mcp-server
└
- Enter the number of seconds goose should wait for actions to complete before timing out. Default is
300seconds.
┌ goose-configure
│
◇ What would you like to configure?
│ Add Extension
│
◇ What type of extension would you like to add?
│ Command-line Extension
│
◇ What would you like to call this extension?
│ Exa Search
│
◇ What command should be run?
│ npx -y exa-mcp-server
│
◆ Please set the timeout for this tool (in secs):
│ 300
└
- Enter a description for this extension.
┌ goose-configure
│
◇ What would you like to configure?
│ Add Extension
│
◇ What type of extension would you like to add?
│ Command-line Extension
│
◇ What would you like to call this extension?
│ Exa Search
│
◇ What command should be run?
│ npx -y exa-mcp-server
│
◇ Please set the timeout for this tool (in secs):
│ 300
│
◆ Enter a description for this extension:
│ AI-powered web search
└
- Add an environment variable for this extension.
info
Obtain your Exa API Key and paste it in. You can get your API key by signing up at exa.ai and navigating to the API keys page in the dashboard.
┌ goose-configure
│
◇ What would you like to configure?
│ Add Extension
│
◇ What type of extension would you like to add?
│ Command-line Extension
│
◇ What would you like to call this extension?
│ Exa Search
│
◇ What command should be run?
│ npx -y exa-mcp-server
│
◇ Please set the timeout for this tool (in secs):
│ 300
│
◇ Enter a description for this extension:
│ AI-powered web search
│
◆ Would you like to add environment variables?
│ Yes
│
◇ Environment variable name:
│ EXA_API_KEY
│
◇ Environment variable value:
│ ▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
│
◇ Add another environment variable?
│ No
│
└ Added Exa Search extension
Example Usage
The Exa MCP server enables AI-powered web search in your goose interactions. Exa offers advanced search capabilities including:
- Neural, keyword, and auto search modes
- Content retrieval with full text, highlights, or summaries
- Category filtering (news, research papers, companies, and more)
- Domain and date range filtering
goose Prompt
Find recent research papers about large language model reasoning capabilities and summarize the key findings.
goose Output
I'll use Exa to search for recent research papers on LLM reasoning.
─── web_search_exa | exa ──────────────────────────
query: large language model reasoning capabilities research paper
category: research paper
Here are the key findings from recent research on LLM reasoning:
1. Chain-of-Thought Reasoning:
- Recent papers show that explicit chain-of-thought prompting significantly improves performance on multi-step reasoning tasks
- Models trained with reasoning traces demonstrate better generalization to novel problem types
2. Mathematical and Logical Reasoning:
- New benchmarks reveal that while LLMs have improved at arithmetic and formal logic, they still struggle with compositional reasoning
- Hybrid approaches combining neural methods with symbolic solvers show promising results
3. Commonsense Reasoning:
- Studies demonstrate that scaling model size continues to improve commonsense reasoning benchmarks
- However, models still fail on tasks requiring physical intuition or temporal reasoning about everyday events
4. Limitations and Future Directions:
- Several papers identify "reasoning shortcuts" where models exploit dataset patterns rather than performing genuine inference
- Research into retrieval-augmented reasoning shows potential for grounding model outputs in verified facts
Would you like me to search for more details on any of these topics?
tip
Exa supports multiple search types including neural search for semantic queries and auto mode that intelligently combines search strategies. You can also filter results by category, domain, and date range for more targeted research.