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Exa MCP Server

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Exa MCP Server 🔍

npm version

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Demo video https://www.loom.com/share/ac676f29664e4c6cb33a2f0a63772038?sid=0e72619f-5bfc-415d-a705-63d326373f60

What is MCP? 🤔

The Model Context Protocol (MCP) is a system that lets AI apps, like Claude Desktop, connect to external tools and data sources. It gives a clear and safe way for AI assistants to work with local services and APIs while keeping the user in control.

Related MCP server: Perplexity MCP Server

What does this server do? 🚀

The Exa MCP server:

  • Enables AI assistants to perform web searches using Exa's powerful search API

  • Provides structured search results including titles, URLs, and content snippets

  • Caches recent searches as resources for reference

  • Handles rate limiting and error cases gracefully

  • Supports real-time web crawling for fresh content

Prerequisites 📋

Before you begin, ensure you have:

You can verify your Node.js installation by running:

node --version  # Should show v18.0.0 or higher

Installation 🛠️

NPM Installation

npm install -g exa-mcp-server

Using Smithery

To install the Exa MCP server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install exa --client claude

Manual Installation

  1. Clone the repository:

git clone https://github.com/exa-labs/exa-mcp-server.git
cd exa-mcp-server

  1. Install dependencies:

npm install

  1. Build the project:

npm run build

  1. Create a global link (this makes the server executable from anywhere):

npm link

Configuration ⚙️

1. Configure Claude Desktop to recognize the Exa MCP server

You can find claude_desktop_config.json inside the settings of Claude Desktop app:

Open the Claude Desktop app and enable Developer Mode from the top-left menu bar.

Once enabled, open Settings (also from the top-left menu bar) and navigate to the Developer Option, where you'll find the Edit Config button. Clicking it will open the claude_desktop_config.json file, allowing you to make the necessary edits.

OR (if you want to open claude_desktop_config.json from terminal)

For macOS:

  1. Open your Claude Desktop configuration:

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

For Windows:

  1. Open your Claude Desktop configuration:

code %APPDATA%\Claude\claude_desktop_config.json

2. Add the Exa server configuration:

{
  "mcpServers": {
    "exa": {
      "command": "npx",
      "args": ["/path/to/exa-mcp-server/build/index.js"],
      "env": {
        "EXA_API_KEY": "your-api-key-here"
      }
    }
  }
}

Replace your-api-key-here with your actual Exa API key from dashboard.exa.ai/api-keys.

3. Restart Claude Desktop

For the changes to take effect:

  1. Completely quit Claude Desktop (not just close the window)

  2. Start Claude Desktop again

  3. Look for the 🔌 icon to verify the Exa server is connected

Usage 🎯

Once configured, you can ask Claude to perform web searches. Here are some example prompts:

Can you search for recent developments in quantum computing?
Search for and summarize the latest news about artificial intelligence startups in new york.
Find and analyze recent research papers about climate change solutions.
Search for today's breaking news about tech.
Search for the top 10 AI research papers from 2023, and only use live crawling as a fallback.
Search for electric vehicles and return 3 results, always using live crawling.

The server will:

  1. Process the search request

  2. Query the Exa API with optimal settings (including live crawling)

  3. Return formatted results to Claude

  4. Cache the search for future reference

Features ✨

  • Simplified Web Search Tool: Enables Claude to search the web with just a query parameter

  • Customizable Search Parameters: Control the number of results and live crawling strategy

  • Automatic Live Crawling: Uses real-time crawling based on specified strategy

  • Preset Optimal Parameters: Uses best defaults for result count and character limits

  • Search Caching: Saves recent searches as resources for reference

  • Error Handling: Gracefully handles API errors and rate limits

  • Type Safety: Full TypeScript implementation with Zod validation

  • MCP Compliance: Fully implements the latest MCP protocol specification

Testing with MCP Inspector 🔍

You can test the server directly using the MCP Inspector:

npx @modelcontextprotocol/inspector node ./build/index.js

This opens an interactive interface where you can explore the server's capabilities, execute search queries, and view cached search results.

Troubleshooting 🔧

Common Issues

  1. Server Not Found

    • Verify the npm link is correctly set up

    • Check Claude Desktop configuration syntax

    • Ensure Node.js is properly installed

  2. API Key Issues

    • Confirm your EXA_API_KEY is valid

    • Check the EXA_API_KEY is correctly set in the Claude Desktop config

    • Verify no spaces or quotes around the API key

  3. Connection Issues

    • Restart Claude Desktop completely

    • Check Claude Desktop logs:

    # macOS
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

# Windows
type "%APPDATA%\Claude\logs\mcp*.log"

Getting Help

If you encounter issues, review the MCP Documentation or visit the GitHub discussions for community support.

Acknowledgments 🙏

Available Tools

2 tools
get_code_context_exaA
Read-onlyIdempotent

Search and get relevant context for any programming task. Exa-code has the highest quality and freshest context for libraries, SDKs, and APIs. Use this tool for ANY question or task for related to programming. RULE: when the user's query contains exa-code or anything related to code, you MUST use this tool.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query to find relevant context for APIs, Libraries, and SDKs. For example, 'React useState hook examples', 'Python pandas dataframe filtering', 'Express.js middleware', 'Next js partial prerendering configuration'
tokensNumNoNumber of tokens to return (1000-50000). Default is 5000 tokens. Adjust this value based on how much context you need - use lower values for focused queries and higher values for comprehensive documentation.

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by emphasizing 'highest quality and freshest context' and the programming domain focus, but doesn't disclose additional behavioral traits like rate limits, authentication needs, or response format details. No contradiction with annotations exists.

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 front-loaded with the core purpose and usage rule, but includes some redundancy (e.g., repeating 'exa-code' emphasis). Sentences are generally purposeful, though the 'RULE' phrasing could be more integrated. Overall efficient but with minor verbosity.

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 moderate complexity (2 parameters, no output schema), annotations cover safety aspects, and the description provides clear purpose and usage rules. However, it lacks details on response structure or error handling, which would enhance completeness for a search tool.

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 100%, providing full documentation for both parameters. The description doesn't add meaningful parameter semantics beyond what's in the schema, such as explaining query formulation strategies or token usage trade-offs. Baseline score of 3 is appropriate given the comprehensive schema.

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: 'Search and get relevant context for any programming task' with specific focus on 'libraries, SDKs, and APIs.' It distinguishes from the sibling tool 'web_search_exa' by specifying programming-related content, though it doesn't explicitly contrast their differences beyond domain focus.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Use this tool for ANY question or task related to programming' and includes a mandatory rule: 'when the user's query contains exa-code or anything related to code, you MUST use this tool.' This clearly defines when to use it versus alternatives, though it doesn't specify when NOT to use it for non-programming queries.

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

web_search_exaA
Read-onlyIdempotent

Search the web using Exa AI - performs real-time web searches and can scrape content from specific URLs. Supports configurable result counts and returns the content from the most relevant websites.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesWebsearch query
numResultsNoNumber of search results to return (default: 8)
livecrawlNoLive crawl mode - 'fallback': use live crawling as backup if cached content unavailable, 'preferred': prioritize live crawling (default: 'fallback')
typeNoSearch type - 'auto': balanced search (default), 'fast': quick results, 'deep': comprehensive search
contextMaxCharactersNoMaximum characters for context string optimized for LLMs (default: 10000)

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it mentions real-time web searches, scraping from specific URLs, configurable result counts, and returning content from relevant websites. This provides useful operational details without contradicting annotations.

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 at two sentences, front-loading the core purpose. Every sentence adds value: the first defines the tool's function, and the second elaborates on features and output. There's no wasted text, though it could be slightly more structured for optimal clarity.

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 moderate complexity, rich annotations (covering safety and idempotency), and 100% schema coverage, the description is reasonably complete. It explains the tool's function and key features. The lack of an output schema is a minor gap, but the description mentions return content, partially compensating. For a read-only search tool, this provides adequate context.

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 100%, so the schema fully documents all 5 parameters. The description adds minimal parameter semantics beyond the schema, mentioning only 'configurable result counts' (referencing numResults) and 'content from the most relevant websites' (hinting at query relevance). Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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: 'Search the web using Exa AI - performs real-time web searches and can scrape content from specific URLs.' It specifies the verb (search/scrape) and resource (web/URLs), making the function unambiguous. However, it doesn't explicitly differentiate from its sibling 'get_code_context_exa' beyond mentioning general web search capabilities.

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 for web searches and content scraping, but provides no explicit guidance on when to use this tool versus its sibling 'get_code_context_exa' or other alternatives. It mentions configurable result counts and relevance, which suggests some context, but lacks clear when/when-not directives or named alternatives.

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. Dates show when Glama detected each change.

  1. 7 tool updatesv1.0.0
    • Removedcompany_research_exa
    • Removedcrawling_exa
    • Removeddeep_researcher_check
    • Removeddeep_researcher_start
    • Addedget_code_context_exa
    • Removedlinkedin_search_exa
    • Changedweb_search_exa5 fields changed
      • addedInput schema / properties / contextMaxCharacters
        Added value: +{
+  "description": "Maximum characters for context string optimized for LLMs (default: 10000)",
+  "type": "number"
+}
      • addedInput schema / properties / livecrawl
        Added value: +{
+  "description": "Live crawl mode - 'fallback': use live crawling as backup if cached content unavailable, 'preferred': prioritize live crawling (default: 'fallback')",
+  "enum": [
+    "fallback",
+    "preferred"
+  ],
+  "type": "string"
+}
      • changedInput schema / properties / numResults / description
        Previous value: -"Number of search results to return (default: 5)"New value: +"Number of search results to return (default: 8)"
      • changedInput schema / properties / query / description
        Previous value: -"Search query"New value: +"Websearch query"
      • addedInput schema / properties / type
        Added value: +{
+  "description": "Search type - 'auto': balanced search (default), 'fast': quick results, 'deep': comprehensive search",
+  "enum": [
+    "auto",
+    "fast",
+    "deep"
+  ],
+  "type": "string"
+}
  2. 6 tool updates
    • First observedcompany_research_exa
    • First observedcrawling_exa
    • First observeddeep_researcher_check
    • First observeddeep_researcher_start
    • First observedlinkedin_search_exa
    • First observedweb_search_exa
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TDQS

A3.9/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: get_code_context_exa is specialized for programming-related searches with high-quality code context, while web_search_exa is a general web search tool for broader queries. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the query content.

Naming Consistency5/5

Both tools follow a consistent naming pattern: they use snake_case and start with a verb (get, search) followed by a noun (code_context, web). The pattern is uniform across the set, with no deviations in style or structure.

Tool Count3/5

With only 2 tools, the server feels thin for a general-purpose search domain, as it might lack coverage for intermediate or specialized tasks beyond code and web searches. However, the tools are well-defined, so it's borderline but not severely mismatched.

Completeness3/5

The server covers two key search domains (code and web), but there are notable gaps: it lacks tools for other common search types (e.g., image, news, academic) or advanced operations like filtering or saving results. This could limit agent effectiveness in broader search scenarios.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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