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Added Exa MCP server documentation and upgraded requirements (#379)
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‎docs/docs.json‎

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"documentation/mcp-server/discord",
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"documentation/mcp-server/doc2markdown",
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"documentation/mcp-server/dropbox",
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"documentation/mcp-server/exa",
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"documentation/mcp-server/firecrawl-deep-research",
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"documentation/mcp-server/firecrawl-web-search",
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"documentation/mcp-server/github",

‎docs/documentation/mcp-server/exa.mdx‎

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---
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title: 'Exa'
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description: 'Learn how to use Klavis to connect your AI application to Exa MCP Server'
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---
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import NoCodeSnippet from '/snippets/mcp-server-card/no-code.mdx';
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<NoCodeSnippet />
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---
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## For Developer
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Follow the instructions below to integrate Exa MCP server to your AI application using our API or SDK.
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### Prerequisites
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- [Create an API key](https://www.klavis.ai/home/api-keys)
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### 1. Create an Exa MCP Server
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Use the following endpoint to create a new remote Exa MCP server instance:
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#### Request
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<CodeGroup>
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```python Python
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from klavis import Klavis
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from klavis.types import McpServerName
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klavis_client = Klavis(api_key="<YOUR_API_KEY>")
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# Create an Exa MCP server instance
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exa_server = klavis_client.mcp_server.create_server_instance(
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server_name=McpServerName.EXA,
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user_id="<YOUR_USER_ID>",
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platform_name="<YOUR_PLATFORM_NAME>",
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)
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```
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```javascript TypeScript
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import { KlavisClient, Klavis } from 'klavis';
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const klavisClient = new KlavisClient({ apiKey: '<YOUR_API_KEY>' });
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// Create an Exa MCP server instance
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const exaServer = await klavisClient.mcpServer.createServerInstance({
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serverName: Klavis.McpServerName.Exa,
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userId: "<YOUR_USER_ID>",
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platformName: "<YOUR_PLATFORM_NAME>",
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});
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```
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```bash cURL
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curl --request POST \
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--url https://api.klavis.ai/mcp-server/instance/create \
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--header 'Authorization: Bearer <YOUR_API_KEY>' \
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--header 'Content-Type: application/json' \
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--data '{
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"serverName": "Exa",
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"userId": "<YOUR_USER_ID>",
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"platformName": "<YOUR_PLATFORM_NAME>"
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}'
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```
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```go Go
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package main
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import (
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"fmt"
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"strings"
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"net/http"
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"io/ioutil"
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)
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func main() {
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url := "https://api.klavis.ai/mcp-server/instance/create"
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payload := strings.NewReader("{\n \"serverName\": \"Exa\",\n \"userId\": \"<YOUR_USER_ID>\",\n \"platformName\": \"<YOUR_PLATFORM_NAME>\"\n}")
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req, _ := http.NewRequest("POST", url, payload)
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req.Header.Add("Authorization", "Bearer <YOUR_API_KEY>")
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req.Header.Add("Content-Type", "application/json")
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res, _ := http.DefaultClient.Do(req)
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defer res.Body.Close()
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body, _ := ioutil.ReadAll(res.Body)
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fmt.Println(res)
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fmt.Println(string(body))
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}
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```
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```java Java
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HttpResponse<String> response = Unirest.post("https://api.klavis.ai/mcp-server/instance/create")
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.header("Authorization", "Bearer <YOUR_API_KEY>")
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.header("Content-Type", "application/json")
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.body("{\n \"serverName\": \"Exa\",\n \"userId\": \"<YOUR_USER_ID>\",\n \"platformName\": \"<YOUR_PLATFORM_NAME>\"\n}")
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.asString();
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```
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</CodeGroup>
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#### Response
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<CodeGroup>
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```json Response
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{
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"serverUrl": "https://exa-mcp-server.klavis.ai/mcp/?instance_id=<instance-id>",
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"instanceId": "<instance-id>"
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}
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```
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</CodeGroup>
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<Check>**serverUrl** specifies the endpoint of the Exa MCP server, which allows you to perform AI-powered semantic search, content retrieval, similarity discovery, and comprehensive research through the Exa Search API.</Check>
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<Note>**instanceId** is used for authentication and identification of your server instance.</Note>
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### 2. Configure Exa API Key
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To use the Exa MCP Server, you need to configure it with your Exa API key. You can get your API key from the [Exa AI website](https://docs.exa.ai/reference/getting-started).
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#### Setting up Exa API Key
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<CodeGroup>
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```bash cURL
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curl --request POST \
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--url https://api.klavis.ai/mcp-server/instance/set-auth \
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--header 'Authorization: Bearer <YOUR_KLAVIS_API_KEY>' \
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--header 'Content-Type: application/json' \
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--data '{
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"instanceId": "<YOUR_INSTANCE_ID>",
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"authData": {
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"token": "<YOUR_EXA_API_KEY>"
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}
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}'
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```
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```python Python
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from klavis import Klavis
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klavis_client = Klavis(api_key="<YOUR_KLAVIS_API_KEY>")
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# Set the Exa API key for your instance
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response = klavis_client.mcp_server.set_instance_auth(
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instance_id="<YOUR_INSTANCE_ID>",
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auth_data={
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"token": "<YOUR_EXA_API_KEY>"
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}
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)
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print(response)
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```
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```javascript TypeScript
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import { KlavisClient } from 'klavis';
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const klavisClient = new KlavisClient({ apiKey: '<YOUR_KLAVIS_API_KEY>' });
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// Set the Exa API key for your instance
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const response = await klavisClient.mcpServer.setInstanceAuth({
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instanceId: "<YOUR_INSTANCE_ID>",
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authData: {
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token: "<YOUR_EXA_API_KEY>"
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}
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});
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console.log(response);
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```
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```go Go
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package main
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import (
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"bytes"
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"encoding/json"
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"fmt"
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"net/http"
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)
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func main() {
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url := "https://api.klavis.ai/mcp-server/instance/set-auth"
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payload := map[string]interface{}{
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"instanceId": "<YOUR_INSTANCE_ID>",
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"authData": map[string]string{
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"token": "<YOUR_EXA_API_KEY>",
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},
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}
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jsonPayload, _ := json.Marshal(payload)
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req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonPayload))
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req.Header.Set("Authorization", "Bearer <YOUR_KLAVIS_API_KEY>")
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req.Header.Set("Content-Type", "application/json")
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client := &http.Client{}
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resp, err := client.Do(req)
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if err != nil {
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panic(err)
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}
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defer resp.Body.Close()
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fmt.Println("Response Status:", resp.Status)
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}
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```
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```java Java
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import java.net.URI;
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import java.net.http.HttpClient;
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import java.net.http.HttpRequest;
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import java.net.http.HttpResponse;
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import java.net.http.HttpResponse.BodyHandlers;
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public class SetAuthToken {
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public static void main(String[] args) {
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try {
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String requestBody = "{\"instanceId\":\"<YOUR_INSTANCE_ID>\",\"authData\":{\"token\":\"<YOUR_EXA_API_KEY>\"}}";
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HttpClient client = HttpClient.newHttpClient();
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HttpRequest request = HttpRequest.newBuilder()
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.uri(URI.create("https://api.klavis.ai/mcp-server/instance/set-auth"))
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.header("Authorization", "Bearer <YOUR_KLAVIS_API_KEY>")
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.header("Content-Type", "application/json")
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.POST(HttpRequest.BodyPublishers.ofString(requestBody))
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.build();
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HttpResponse<String> response = client.send(request, BodyHandlers.ofString());
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System.out.println("Response: " + response.body());
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} catch (Exception e) {
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e.printStackTrace();
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}
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}
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}
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```
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</CodeGroup>
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#### Response
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<CodeGroup>
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```json Response
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{
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"success": true,
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"message": "<string>"
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}
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```
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</CodeGroup>
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### Explore MCP Server Tools
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<Accordion title="Tools Information">
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| Tool Name | Description |
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|-----------------------|--------------------------------------------------------------------------|
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| exa_search | Perform AI-powered semantic search across the web using neural or keyword search with advanced filtering options including domains, dates, and content patterns |
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| exa_get_contents | Retrieve clean, parsed HTML content from web pages using their Exa result IDs with optional highlighting and summarization |
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| exa_find_similar | Discover web pages that are semantically similar to a given URL based on content meaning and context |
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| exa_answer | Get direct, focused answers to specific questions by searching and analyzing web sources with citations |
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| exa_research | Conduct comprehensive, multi-source research on topics with structured analysis and detailed citations |
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</Accordion>
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<Note>For more details about tool input schema, use the [list_tool](https://docs.klavis.ai/api-reference/mcp-server/list-tools) API.</Note>

‎mcp_servers/exa/requirements.txt‎

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mcp==1.12.2
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exa_py==1.0.12
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python-dotenv==1.0.0
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httpx>=0.24.0
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click>=8.0.0
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starlette>=0.27.0
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uvicorn>=0.23.1
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mcp==1.13.1
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exa_py==1.15.1
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python-dotenv==1.1.1
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httpx>=0.28.1
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click>=8.2.1
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starlette>=0.47.2
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uvicorn>=0.35.0

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