Add pt-BR docs translation (#3039)

* docs: add pt-br translations

Powered by a CrewAI Flow https://github.com/danielfsbarreto/docs_translator

* Update mcp/overview.mdx brazilian docs

Its en-US counterpart was updated after I did a pass,
so now it includes the new section about @CrewBase

docs/en/concepts/agents.mdx

@@ -71,7 +71,7 @@ There are two ways to create agents in CrewAI: using **YAML configuration (recom
 
 Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
+After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
 
 <Note>
 Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:

docs/en/concepts/crews.mdx

@@ -45,7 +45,7 @@ There are two ways to create crews in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
+After creating your CrewAI project as outlined in the [Installation](/en/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
 
 #### Example Crew Class with Decorators
 

docs/en/concepts/tasks.mdx

@@ -66,7 +66,7 @@ There are two ways to create tasks in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
+After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
 
 <Note>
 Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:

docs/en/enterprise/guides/build-crew.mdx

@@ -23,13 +23,13 @@ icon: "people-arrows"
 
 ### Installation and Setup
 
-<Card title="Follow Standard Installation" icon="wrench" href="/installation">
+<Card title="Follow Standard Installation" icon="wrench" href="/en/installation">
   Follow our standard installation guide to set up CrewAI CLI and create your first project.
 </Card>
 
 ### Building Your Crew
 
-<Card title="Quickstart Tutorial" icon="rocket" href="/quickstart">
+<Card title="Quickstart Tutorial" icon="rocket" href="/en/quickstart">
   Follow our quickstart guide to create your first agent crew using YAML configuration.
 </Card>
 

docs/en/enterprise/guides/deploy-crew.mdx

@@ -132,7 +132,7 @@ You can also deploy your crews directly through the CrewAI Enterprise web interf
 
   <Step title="Pushing to GitHub">
 
-  You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/quickstart).
+  You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/en/quickstart).
 
   </Step>
 

docs/en/enterprise/introduction.mdx

@@ -69,7 +69,7 @@ CrewAI Enterprise extends the power of the open-source framework with features d
     <Card
       title="Build Crew"
       icon="paintbrush"
-      href="/enterprise/guides/build-crew"
+      href="/en/enterprise/guides/build-crew"
     >
       Build Crew
     </Card>
@@ -79,7 +79,7 @@ CrewAI Enterprise extends the power of the open-source framework with features d
     <Card
       title="Deploy Crew"
       icon="rocket"
-      href="/enterprise/guides/deploy-crew"
+      href="/en/enterprise/guides/deploy-crew"
     >
       Deploy Crew
     </Card>
@@ -89,11 +89,11 @@ CrewAI Enterprise extends the power of the open-source framework with features d
     <Card
       title="API Access"
       icon="code"
-      href="/enterprise/guides/use-crew-api"
+      href="/en/enterprise/guides/kickoff-crew"
     >
       Use the Crew API
     </Card>
   </Step>
 </Steps>
 
-For detailed instructions, check out our [deployment guide](/enterprise/guides/deploy-crew) or click the button below to get started.
+For detailed instructions, check out our [deployment guide](/en/enterprise/guides/deploy-crew) or click the button below to get started.

docs/en/enterprise/resources/frequently-asked-questions.mdx

@@ -48,7 +48,7 @@ icon: "circle-question"
 
         To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
 
-        For detailed implementation guidance, see our [Human-in-the-Loop guide](/how-to/human-in-the-loop).
+        For detailed implementation guidance, see our [Human-in-the-Loop guide](/en/how-to/human-in-the-loop).
     </Accordion>
 
     <Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">

docs/en/guides/advanced/customizing-prompts.mdx

@@ -174,13 +174,13 @@ agent = Agent(
 
 For production transparency, integrate with observability platforms to monitor all prompts and LLM interactions. This allows you to see exactly what prompts (including default instructions) are being sent to your LLMs.
 
-See our [Observability documentation](/how-to/observability) for detailed integration guides with various platforms including Langfuse, MLflow, Weights & Biases, and custom logging solutions.
+See our [Observability documentation](/en/observability/overview) for detailed integration guides with various platforms including Langfuse, MLflow, Weights & Biases, and custom logging solutions.
 
 ### Best Practices for Production
 
 1. **Always inspect generated prompts** before deploying to production
 2. **Use custom templates** when you need full control over prompt content
-3. **Integrate observability tools** for ongoing prompt monitoring (see [Observability docs](/how-to/observability))
+3. **Integrate observability tools** for ongoing prompt monitoring (see [Observability docs](/en/observability/overview))
 4. **Test with different LLMs** as default instructions may work differently across models
 5. **Document your prompt customizations** for team transparency
 

docs/en/guides/agents/crafting-effective-agents.mdx

@@ -448,5 +448,5 @@ Congratulations! You now understand the principles and practices of effective ag
 ## Next Steps
 
 - Experiment with different agent configurations for your specific use case
-- Learn about [building your first crew](/guides/crews/first-crew) to see how agents work together
-- Explore [CrewAI Flows](/guides/flows/first-flow) for more advanced orchestration
+- Learn about [building your first crew](/en/guides/crews/first-crew) to see how agents work together
+- Explore [CrewAI Flows](/en/guides/flows/first-flow) for more advanced orchestration

docs/en/guides/concepts/evaluating-use-cases.mdx

@@ -11,7 +11,7 @@ When building AI applications with CrewAI, one of the most important decisions y
 At the heart of this decision is understanding the relationship between **complexity** and **precision** in your application:
 
 <Frame caption="Complexity vs. Precision Matrix for CrewAI Applications">
-  <img src="../../images/complexity_precision.png" alt="Complexity vs. Precision Matrix" />
+  <img src="/images/complexity_precision.png" alt="Complexity vs. Precision Matrix" />
 </Frame>
 
 This matrix helps visualize how different approaches align with varying requirements for complexity and precision. Let's explore what each quadrant means and how it guides your architectural choices.
@@ -497,7 +497,7 @@ You now have a framework for evaluating CrewAI use cases and choosing the right
 
 ## Next Steps
 
-- Learn more about [crafting effective agents](/guides/agents/crafting-effective-agents)
-- Explore [building your first crew](/guides/crews/first-crew)
-- Dive into [mastering flow state management](/guides/flows/mastering-flow-state)
-- Check out the [core concepts](/concepts/agents) for deeper understanding
+- Learn more about [crafting effective agents](/en/guides/agents/crafting-effective-agents)
+- Explore [building your first crew](/en/guides/crews/first-crew)
+- Dive into [mastering flow state management](/en/guides/flows/mastering-flow-state)
+- Check out the [core concepts](/en/concepts/agents) for deeper understanding

docs/en/guides/crews/first-crew.mdx

@@ -32,9 +32,9 @@ Let's get started building your first crew!
 
 Before starting, make sure you have:
 
-1. Installed CrewAI following the [installation guide](/installation)
+1. Installed CrewAI following the [installation guide](/en/installation)
 2. Set up your LLM API key in your environment, following the [LLM setup
-   guide](/concepts/llms#setting-up-your-llm)
+   guide](/en/concepts/llms#setting-up-your-llm)
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Project
@@ -54,7 +54,7 @@ This will generate a project with the basic structure needed for your crew. The
 - A main script to run the crew
 
 <Frame caption="CrewAI Framework Overview">
-  <img src="../../images/crews.png" alt="CrewAI Framework Overview" />
+  <img src="/images/crews.png" alt="CrewAI Framework Overview" />
 </Frame>
 
 
@@ -287,7 +287,7 @@ SERPER_API_KEY=your_serper_api_key
 # Add your provider's API key here too.
 ```
 
-See the [LLM Setup guide](/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).
+See the [LLM Setup guide](/en/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).
 
 ## Step 8: Install Dependencies
 
@@ -388,7 +388,7 @@ Now that you've built your first crew, you can:
 2. Try more complex task structures and workflows
 3. Implement custom tools to give your agents new capabilities
 4. Apply your crew to different topics or problem domains
-5. Explore [CrewAI Flows](/guides/flows/first-flow) for more advanced workflows with procedural programming
+5. Explore [CrewAI Flows](/en/guides/flows/first-flow) for more advanced workflows with procedural programming
 
 <Check>
 Congratulations! You've successfully built your first CrewAI crew that can research and analyze any topic you provide. This foundational experience has equipped you with the skills to create increasingly sophisticated AI systems that can tackle complex, multi-stage problems through collaborative intelligence.

docs/en/guides/flows/first-flow.mdx

@@ -42,9 +42,9 @@ Let's dive in and build your first flow!
 
 Before starting, make sure you have:
 
-1. Installed CrewAI following the [installation guide](/installation)
+1. Installed CrewAI following the [installation guide](/en/installation)
 2. Set up your LLM API key in your environment, following the [LLM setup
-   guide](/concepts/llms#setting-up-your-llm)
+   guide](/en/concepts/llms#setting-up-your-llm)
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Flow Project
@@ -59,7 +59,7 @@ cd guide_creator_flow
 This will generate a project with the basic structure needed for your flow.
 
 <Frame caption="CrewAI Framework Overview">
-  <img src="../../images/flows.png" alt="CrewAI Framework Overview" />
+  <img src="/images/flows.png" alt="CrewAI Framework Overview" />
 </Frame>
 
 ## Step 2: Understanding the Project Structure
@@ -443,7 +443,7 @@ This is the power of flows - combining different types of processing (user inter
 ## Step 6: Set Up Your Environment Variables
 
 Create a `.env` file in your project root with your API keys. See the [LLM setup
-guide](/concepts/llms#setting-up-your-llm) for details on configuring a provider.
+guide](/en/concepts/llms#setting-up-your-llm) for details on configuring a provider.
 
 ```sh .env
 OPENAI_API_KEY=your_openai_api_key

docs/en/guides/flows/mastering-flow-state.mdx

@@ -767,5 +767,5 @@ You've now mastered the concepts and practices of state management in CrewAI Flo
 
 - Experiment with both structured and unstructured state in your flows
 - Try implementing state persistence for long-running workflows
-- Explore [building your first crew](/guides/crews/first-crew) to see how crews and flows can work together
-- Check out the [Flow reference documentation](/concepts/flows) for more advanced features
+- Explore [building your first crew](/en/guides/crews/first-crew) to see how crews and flows can work together
+- Check out the [Flow reference documentation](/en/concepts/flows) for more advanced features

docs/en/installation.mdx

@@ -186,7 +186,7 @@ For teams and organizations, CrewAI offers enterprise deployment options that el
   <Card
     title="Build Your First Agent"
     icon="code"
-    href="/quickstart"
+    href="/en/quickstart"
   >
     Follow our quickstart guide to create your first CrewAI agent and get hands-on experience.
   </Card>

docs/en/introduction.mdx

@@ -10,8 +10,8 @@ icon: handshake
 
 CrewAI empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario:
 
-- **[CrewAI Crews](/guides/crews/first-crew)**: Optimize for autonomy and collaborative intelligence, enabling you to create AI teams where each agent has specific roles, tools, and goals.
-- **[CrewAI Flows](/guides/flows/first-flow)**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively.
+- **[CrewAI Crews](/en/guides/crews/first-crew)**: Optimize for autonomy and collaborative intelligence, enabling you to create AI teams where each agent has specific roles, tools, and goals.
+- **[CrewAI Flows](/en/guides/flows/first-flow)**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively.
 
 With over 100,000 developers certified through our community courses, CrewAI is rapidly becoming the standard for enterprise-ready AI automation.
 
@@ -23,7 +23,7 @@ With over 100,000 developers certified through our community courses, CrewAI is
 </Note>
 
 <Frame caption="CrewAI Framework Overview">
-  <img src="images/crews.png" alt="CrewAI Framework Overview" />
+  <img src="/images/crews.png" alt="CrewAI Framework Overview" />
 </Frame>
 
 | Component | Description | Key Features |
@@ -64,7 +64,7 @@ With over 100,000 developers certified through our community courses, CrewAI is
 </Note>
 
 <Frame caption="CrewAI Framework Overview">
-  <img src="images/flows.png" alt="CrewAI Framework Overview" />
+  <img src="/images/flows.png" alt="CrewAI Framework Overview" />
 </Frame>
 
 | Component | Description | Key Features |
@@ -94,21 +94,21 @@ With over 100,000 developers certified through our community courses, CrewAI is
 ## When to Use Crews vs. Flows
 
 <Note>
-  Understanding when to use [Crews](/guides/crews/first-crew) versus [Flows](/guides/flows/first-flow) is key to maximizing the potential of CrewAI in your applications.
+  Understanding when to use [Crews](/en/guides/crews/first-crew) versus [Flows](/en/guides/flows/first-flow) is key to maximizing the potential of CrewAI in your applications.
 </Note>
 
 | Use Case | Recommended Approach | Why? |
 |:---------|:---------------------|:-----|
-| **Open-ended research** | [Crews](/guides/crews/first-crew) | When tasks require creative thinking, exploration, and adaptation |
-| **Content generation** | [Crews](/guides/crews/first-crew) | For collaborative creation of articles, reports, or marketing materials |
-| **Decision workflows** | [Flows](/guides/flows/first-flow) | When you need predictable, auditable decision paths with precise control |
-| **API orchestration** | [Flows](/guides/flows/first-flow) | For reliable integration with multiple external services in a specific sequence |
-| **Hybrid applications** | Combined approach | Use [Flows](/guides/flows/first-flow) to orchestrate overall process with [Crews](/guides/crews/first-crew) handling complex subtasks |
+| **Open-ended research** | [Crews](/en/guides/crews/first-crew) | When tasks require creative thinking, exploration, and adaptation |
+| **Content generation** | [Crews](/en/guides/crews/first-crew) | For collaborative creation of articles, reports, or marketing materials |
+| **Decision workflows** | [Flows](/en/guides/flows/first-flow) | When you need predictable, auditable decision paths with precise control |
+| **API orchestration** | [Flows](/en/guides/flows/first-flow) | For reliable integration with multiple external services in a specific sequence |
+| **Hybrid applications** | Combined approach | Use [Flows](/en/guides/flows/first-flow) to orchestrate overall process with [Crews](/en/guides/crews/first-crew) handling complex subtasks |
 
 ### Decision Framework
 
-- **Choose [Crews](/guides/crews/first-crew) when:** You need autonomous problem-solving, creative collaboration, or exploratory tasks
-- **Choose [Flows](/guides/flows/first-flow) when:** You require deterministic outcomes, auditability, or precise control over execution
+- **Choose [Crews](/en/guides/crews/first-crew) when:** You need autonomous problem-solving, creative collaboration, or exploratory tasks
+- **Choose [Flows](/en/guides/flows/first-flow) when:** You require deterministic outcomes, auditability, or precise control over execution
 - **Combine both when:** Your application needs both structured processes and pockets of autonomous intelligence
 
 ## Why Choose CrewAI?
@@ -126,14 +126,14 @@ With over 100,000 developers certified through our community courses, CrewAI is
   <Card
     title="Build Your First Crew"
     icon="users-gear"
-    href="/guides/crews/first-crew"
+    href="/en/guides/crews/first-crew"
   >
     Step-by-step tutorial to create a collaborative AI team that works together to solve complex problems.
   </Card>
   <Card
     title="Build Your First Flow"
     icon="diagram-project"
-    href="/guides/flows/first-flow"
+    href="/en/guides/flows/first-flow"
   >
     Learn how to create structured, event-driven workflows with precise control over execution.
   </Card>
@@ -143,14 +143,14 @@ With over 100,000 developers certified through our community courses, CrewAI is
   <Card
     title="Install CrewAI"
     icon="wrench"
-    href="/installation"
+    href="/en/installation"
   >
     Get started with CrewAI in your development environment.
   </Card>
   <Card
     title="Quick Start"
     icon="bolt"
-    href="/quickstart"
+    href="en/quickstart"
   >
     Follow our quickstart guide to create your first CrewAI agent and get hands-on experience.
   </Card>

docs/en/learn/overview.mdx

@@ -12,38 +12,38 @@ This section provides comprehensive guides and tutorials to help you master Crew
 
 ### Core Concepts
 <CardGroup cols={2}>
-  <Card title="Sequential Process" icon="list-ol" href="/learn/sequential-process">
+  <Card title="Sequential Process" icon="list-ol" href="/en/learn/sequential-process">
     Learn how to execute tasks in a sequential order for structured workflows.
   </Card>
 
-  <Card title="Hierarchical Process" icon="sitemap" href="/learn/hierarchical-process">
+  <Card title="Hierarchical Process" icon="sitemap" href="/en/learn/hierarchical-process">
     Implement hierarchical task execution with manager agents overseeing workflows.
   </Card>
 
-  <Card title="Conditional Tasks" icon="code-branch" href="/learn/conditional-tasks">
+  <Card title="Conditional Tasks" icon="code-branch" href="/en/learn/conditional-tasks">
     Create dynamic workflows with conditional task execution based on outcomes.
   </Card>
 
-  <Card title="Async Kickoff" icon="bolt" href="/learn/kickoff-async">
+  <Card title="Async Kickoff" icon="bolt" href="/en/learn/kickoff-async">
     Execute crews asynchronously for improved performance and concurrency.
   </Card>
 </CardGroup>
 
 ### Agent Development
 <CardGroup cols={2}>
-  <Card title="Customizing Agents" icon="user-gear" href="/learn/customizing-agents">
+  <Card title="Customizing Agents" icon="user-gear" href="/en/learn/customizing-agents">
     Learn how to customize agent behavior, roles, and capabilities.
   </Card>
 
-  <Card title="Coding Agents" icon="code" href="/learn/coding-agents">
+  <Card title="Coding Agents" icon="code" href="/en/learn/coding-agents">
     Build agents that can write, execute, and debug code automatically.
   </Card>
 
-  <Card title="Multimodal Agents" icon="images" href="/learn/multimodal-agents">
+  <Card title="Multimodal Agents" icon="images" href="/en/learn/multimodal-agents">
     Create agents that can process text, images, and other media types.
   </Card>
 
-  <Card title="Custom Manager Agent" icon="user-tie" href="/learn/custom-manager-agent">
+  <Card title="Custom Manager Agent" icon="user-tie" href="/en/learn/custom-manager-agent">
     Implement custom manager agents for complex hierarchical workflows.
   </Card>
 </CardGroup>
@@ -52,38 +52,38 @@ This section provides comprehensive guides and tutorials to help you master Crew
 
 ### Workflow Control
 <CardGroup cols={2}>
-  <Card title="Human in the Loop" icon="user-check" href="/learn/human-in-the-loop">
+  <Card title="Human in the Loop" icon="user-check" href="/en/learn/human-in-the-loop">
     Integrate human oversight and intervention into agent workflows.
   </Card>
 
-  <Card title="Human Input on Execution" icon="hand-paper" href="/learn/human-input-on-execution">
+  <Card title="Human Input on Execution" icon="hand-paper" href="/en/learn/human-input-on-execution">
     Allow human input during task execution for dynamic decision making.
   </Card>
 
-  <Card title="Replay Tasks" icon="rotate-left" href="/learn/replay-tasks-from-latest-crew-kickoff">
+  <Card title="Replay Tasks" icon="rotate-left" href="/en/learn/replay-tasks-from-latest-crew-kickoff">
     Replay and resume tasks from previous crew executions.
   </Card>
 
-  <Card title="Kickoff for Each" icon="repeat" href="/learn/kickoff-for-each">
+  <Card title="Kickoff for Each" icon="repeat" href="/en/learn/kickoff-for-each">
     Execute crews multiple times with different inputs efficiently.
   </Card>
 </CardGroup>
 
 ### Customization & Integration
 <CardGroup cols={2}>
-  <Card title="Custom LLM" icon="brain" href="/learn/custom-llm">
+  <Card title="Custom LLM" icon="brain" href="/en/learn/custom-llm">
     Integrate custom language models and providers with CrewAI.
   </Card>
 
-  <Card title="LLM Connections" icon="link" href="/learn/llm-connections">
+  <Card title="LLM Connections" icon="link" href="/en/learn/llm-connections">
     Configure and manage connections to various LLM providers.
   </Card>
 
-  <Card title="Create Custom Tools" icon="wrench" href="/learn/create-custom-tools">
+  <Card title="Create Custom Tools" icon="wrench" href="/en/learn/create-custom-tools">
     Build custom tools to extend agent capabilities.
   </Card>
 
-  <Card title="Using Annotations" icon="at" href="/learn/using-annotations">
+  <Card title="Using Annotations" icon="at" href="/en/learn/using-annotations">
     Use Python annotations for cleaner, more maintainable code.
   </Card>
 </CardGroup>
@@ -92,18 +92,18 @@ This section provides comprehensive guides and tutorials to help you master Crew
 
 ### Content & Media
 <CardGroup cols={2}>
-  <Card title="DALL-E Image Generation" icon="image" href="/learn/dalle-image-generation">
+  <Card title="DALL-E Image Generation" icon="image" href="/en/learn/dalle-image-generation">
     Generate images using DALL-E integration with your agents.
   </Card>
 
-  <Card title="Bring Your Own Agent" icon="user-plus" href="/learn/bring-your-own-agent">
+  <Card title="Bring Your Own Agent" icon="user-plus" href="/en/learn/bring-your-own-agent">
     Integrate existing agents and models into CrewAI workflows.
   </Card>
 </CardGroup>
 
 ### Tool Management
 <CardGroup cols={2}>
-  <Card title="Force Tool Output as Result" icon="hammer" href="/learn/force-tool-output-as-result">
+  <Card title="Force Tool Output as Result" icon="hammer" href="/en/learn/force-tool-output-as-result">
     Configure tools to return their output directly as task results.
   </Card>
 </CardGroup>

docs/en/mcp/overview.mdx

@@ -142,7 +142,7 @@ class CrewWithMCP:
   <Card
     title="Stdio Transport"
     icon="server"
-    href="/mcp/stdio"
+    href="/en/mcp/stdio"
     color="#3B82F6"
   >
     Connect to local MCP servers via standard input/output. Ideal for scripts and local executables.
@@ -150,7 +150,7 @@ class CrewWithMCP:
   <Card
     title="SSE Transport"
     icon="wifi"
-    href="/mcp/sse"
+    href="/en/mcp/sse"
     color="#10B981"
   >
     Integrate with remote MCP servers using Server-Sent Events for real-time data streaming.
@@ -158,7 +158,7 @@ class CrewWithMCP:
   <Card
     title="Streamable HTTP Transport"
     icon="globe"
-    href="/mcp/streamable-http"
+    href="/en/mcp/streamable-http"
     color="#F59E0B"
   >
     Utilize flexible Streamable HTTP for robust communication with remote MCP servers.
@@ -166,7 +166,7 @@ class CrewWithMCP:
   <Card
     title="Connecting to Multiple Servers"
     icon="layer-group"
-    href="/mcp/multiple-servers"
+    href="/en/mcp/multiple-servers"
     color="#8B5CF6"
   >
     Aggregate tools from several MCP servers simultaneously using a single adapter.
@@ -174,7 +174,7 @@ class CrewWithMCP:
   <Card
     title="Security Considerations"
     icon="lock"
-    href="/mcp/security"
+    href="/en/mcp/security"
     color="#EF4444"
   >
     Review important security best practices for MCP integration to keep your agents safe.