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jackedelic

exa-search

by jackedelic
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TypeScript
Remote

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.

Remote Exa MCP 🌐

Connect directly to Exa's hosted MCP server (instead of running it locally).

Remote Exa MCP URL

https://mcp.exa.ai/mcp?exaApiKey=your-exa-api-key

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

Claude Desktop Configuration for Remote MCP

Add this to your Claude Desktop configuration file:

{
  "mcpServers": {
    "exa": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.exa.ai/mcp?exaApiKey=your-exa-api-key"
      ]
    }
  }
}

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

Related MCP server: MCP Web Research Server

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": ["-y", "exa-mcp-server"],
      "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. Available Tools & Tool Selection

The Exa MCP server includes the following tools, which can be enabled by adding the --tools:

  • web_search_exa: Performs real-time web searches with optimized results and content extraction.

  • research_paper_search: Specialized search focused on academic papers and research content.

  • company_research: Comprehensive company research tool that crawls company websites to gather detailed information about businesses.

  • crawling: Extracts content from specific URLs, useful for reading articles, PDFs, or any web page when you have the exact URL.

  • competitor_finder: Identifies competitors of a company by searching for businesses offering similar products or services.

  • linkedin_search: Search LinkedIn for companies and people using Exa AI. Simply include company names, person names, or specific LinkedIn URLs in your query.

  • wikipedia_search_exa: Search and retrieve information from Wikipedia articles on specific topics, giving you accurate, structured knowledge from the world's largest encyclopedia.

  • github_search: Search GitHub repositories using Exa AI - performs real-time searches on GitHub.com to find relevant repositories, issues, and GitHub accounts.

You can choose which tools to enable by adding the --tools parameter to your Claude Desktop configuration:

Specify which tools to enable:

{
  "mcpServers": {
    "exa": {
      "command": "npx",
      "args": [
        "-y",
        "exa-mcp-server",
        "--tools=web_search_exa,research_paper_search,company_research,crawling,competitor_finder,linkedin_search,wikipedia_search_exa,github_search"
      ],
      "env": {
        "EXA_API_KEY": "your-api-key-here"
      }
    }
  }
}

For enabling multiple tools, use a comma-separated list:

{
  "mcpServers": {
    "exa": {
      "command": "npx",
      "args": [
        "-y",
        "exa-mcp-server",
        "--tools=web_search_exa,research_paper_search,company_research,crawling,competitor_finder,linkedin_search,wikipedia_search_exa,github_search"
      ],
      "env": {
        "EXA_API_KEY": "your-api-key-here"
      }
    }
  }
}

If you don't specify any tools, all tools enabled by default will be used.

4. 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

Using via NPX

If you prefer to run the server directly, you can use npx:

# Run with all tools enabled by default
npx exa-mcp-server

# Enable specific tools only
npx exa-mcp-server --tools=web_search_exa

# Enable multiple tools
npx exa-mcp-server --tools=web_search_exa,research_paper_search

# List all available tools
npx exa-mcp-server --list-tools

Troubleshooting 🔧

Common Issues

  1. Server Not Found

    • Verify the npm link is correctly set up

    • Check Claude Desktop configuration syntax (json file)

  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:

Built with ❤️ by team Exa

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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, repeatable read operation. The description adds value by mentioning '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 beyond what annotations provide.

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 with three sentences that each serve a purpose: stating the tool's purpose, highlighting quality/freshness, and providing usage rules. It's front-loaded with the core function, though the mandatory rule could be more concise.

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), the description is reasonably complete. It covers purpose, domain, quality claims, and usage rules. With annotations handling safety profiles and schema covering parameters, the main gap is lack of output format explanation, but this is acceptable since no output schema exists.

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 both parameters (query and tokensNum) with descriptions and examples. The description doesn't add any parameter-specific semantics beyond what's in the schema, maintaining the baseline score of 3 since the schema carries the full burden.

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 resources mentioned ('libraries, SDKs, and APIs'). It distinguishes from the sibling tool 'web_search_exa' by specifying programming-related queries, 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 guidelines: '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 gives clear when-to-use instructions, though it doesn't specify when NOT to use it or alternatives beyond the sibling tool.

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

A4.2/5.0
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 specifies real-time capability, scraping functionality, configurable result counts, and that it returns content from relevant websites. 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.

Conciseness5/5

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

The description is front-loaded with the core purpose in the first sentence, followed by supporting details. Every sentence adds value: the first establishes the main function, the second explains scraping capability, and the third covers configurability and return behavior. No wasted words or redundancy.

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?

For a search tool with rich annotations (covering safety and idempotency) and 100% schema coverage, the description provides good contextual completeness. It explains the tool's real-time nature, scraping capability, and return behavior. The main gap is the absence of an output schema, but the description partially compensates by mentioning what gets returned ('content from the most relevant websites').

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 detailed documentation for all 5 parameters. The description mentions 'configurable result counts' which aligns with the 'numResults' parameter, but doesn't add significant meaning beyond what the schema already specifies. Baseline 3 is appropriate given the comprehensive schema coverage.

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 tool's purpose with specific verbs ('search the web', 'scrape content') and resources ('Exa AI', 'web', 'specific URLs'). It distinguishes from the sibling tool 'get_code_context_exa' by focusing on general web search rather than code-specific context retrieval.

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 provides clear context for when to use this tool ('performs real-time web searches', 'can scrape content from specific URLs'), but doesn't explicitly state when not to use it or mention alternatives beyond the implied distinction from the sibling tool. It lacks explicit exclusions or comparison to other search methods.

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: one is specialized for programming-related searches (code context), while the other is for general web searches. There is no overlap in functionality, and the descriptions explicitly differentiate their use cases.

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 phrase (code_context, web). This makes them predictable and easy to understand.

Tool Count2/5

With only two tools, the server feels under-scoped for a search domain. While the tools cover distinct areas (code and web), a search server typically benefits from more granular operations like filtering, pagination, or specialized search types, making this set feel thin.

Completeness3/5

The tools cover two key search domains (code and web), but there are notable gaps. For example, there are no tools for advanced search configurations, result management, or handling different content types beyond code and general web pages, which limits flexibility for agents.

Maintenance

ActivityInactive
ResponsivenessSyncing

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