Managing AI tools across multiple servers can quickly overload your context window, wasting tokens and limiting efficiency. The MCP Bundler solves this by letting you control which servers and tools to load, ensuring only relevant ones are activated for your tasks. Here’s how it works:
- Selective Loading: Activate only the servers and tools you need, reducing token usage and keeping your AI focused.
- Custom Bundles: Save tool configurations for specific projects and switch between them instantly.
- Dynamic Tool Management: Use search and call tools to load only the necessary tools when required, keeping context lean.
- Temporary File Storage: Manage large tool outputs without overloading your context.
- Performance Monitoring: Real-time health indicators and logs help identify and resolve inefficiencies.
Model Context Protocol: A Deep Dive into the future of AI systems
Activating Only the Servers and Tools You Need
To keep things efficient, it’s crucial to load only the servers and tools you actually need. By doing this, you can reduce token overhead and keep your AI focused on the task at hand without unnecessary distractions.
Using the Control Panel to Toggle Servers and Tools
The MCP Bundler makes managing your setup simple with its unified control panel. This interface displays all your configured servers and tools, allowing you to toggle servers on or off with just one click. This means you can fine-tune your setup before starting a conversation, ensuring only the essentials are activated.
The control panel provides a clear view of your MCP servers, their status, and the tools they offer. If a server isn’t needed, you can deactivate it, ensuring its tools won’t load during your next session. For even greater precision, you can manage individual tools within a server – disabling the ones irrelevant to your current task while keeping the useful ones active.
Why does this matter? The benefits of selective loading are substantial. In November 2025, Kelly Kohlleffel from Fivetran‘s Global Partner Sales Engineering team faced severe context limitations while juggling multiple MCP servers. He explained:
"Claude Desktop loaded ALL configured servers, whether I needed them or not. Moving between servers required reloading contexts, and each API call added significant overhead."
To solve this, Kohlleffel created a simple toggle application to modify the configuration file before launching Claude Desktop. By activating only the servers he needed, he cut his initial token overhead from 37,600 tokens to just 3,000. His key takeaway?
"A key learning for me was that context window management starts before the conversation begins. I need to load only what’s needed for the workflow."
The MCP Bundler now integrates this functionality directly into its control panel, letting you adjust your setup in seconds – no custom scripts or manual edits required. You can launch your AI client with only the components you need, keeping token overhead to a minimum before you even start.
For repeated tasks or workflows, creating bundles can make this process even more efficient.
Creating Custom Tool Bundles for Different Projects
Custom tool bundles are a game-changer for project-specific workflows. Imagine you’re working on a web development project – this might require servers for file system access, browser automation, and Git operations. On the other hand, a data analysis project could call for database connections and visualization tools. Instead of toggling servers manually for each project, MCP Bundler lets you save these setups as reusable bundles.
A bundle is essentially a saved configuration that specifies which servers and tools should be active for a particular task. Once you’ve arranged your setup for a specific project, you can save it with a descriptive name. When you return to a similar project, you simply load the bundle – no need to start from scratch.
This approach isn’t just about convenience – it’s about efficiency. Kohlleffel demonstrated this by consolidating 188 individual tools into just five workflow-focused tools within a single "Pipeline Health MCP Server." This shift reduced tool schema overhead from 37,600 tokens to around 1,000 – a massive 97% reduction.
Managing Context with Search Tool and Call Tool
When working with essential servers, it’s easy for tool schemas to pile up. Each tool comes with its own schema, description, and parameters, all of which take up valuable tokens before you even begin your actual task. To tackle this, MCP Bundler offers two specialized tools: search_tool and call_tool. These tools work together to keep your context streamlined by hiding most tool data until it’s actually needed.
This system uses a two-step process to delay full tool loading. First, the AI searches for relevant tools based on the task at hand. Then, it calls and loads only the specific tool it needs. This "just-in-time" approach prevents unnecessary data from cluttering your context window, saving tokens and making operations more efficient.
How Search Tool Works
The search_tool functions like a directory for MCP tools. When the AI needs to perform a task, it queries the search_tool using keywords or a brief description of the task. The search_tool then scans all available tools and returns only the ones that match the query, along with their instructions and parameters.
This means the AI doesn’t need to keep track of every tool in your system. For instance, a web scraping tool won’t take up space in your context when you’re working on database queries, and file system tools stay hidden when you’re analyzing data. Tools are discovered dynamically, right when they’re needed.
What makes this even more efficient is that the search_tool provides more than just tool names. It delivers the complete instructions for each matching tool, including parameter details, expected formats, and descriptions. This ensures the AI has everything it needs to use the tool correctly, but only for the tools relevant to the current task. This dynamic discovery process sets the stage for smooth execution through the call tool.
Using Call Tool for Execution
Once the search_tool identifies the right tool, the AI uses call_tool to execute it. The call_tool activates the specific tool by name, passes any required parameters, and retrieves the results.
This method ensures that only the outputs you need are loaded into your context. Instead of dealing with multiple tool outputs all at once, the AI processes one result at a time. The tool is called, the output is received, and then the AI moves on. This sequential process keeps your context clean and organized, with inactive tools staying hidden and not consuming any extra tokens.
By isolating each tool interaction, the AI avoids distractions from irrelevant data and maintains its focus on the task. This approach is especially useful when dealing with multiple MCP servers, each offering numerous tools. Without this system, your context could easily become overwhelmed with unnecessary tool documentation before you even begin your work.
The combination of search_tool and call_tool creates an efficient, on-demand tool loading system. Tools appear only when needed and disappear when their job is done, conserving tokens and keeping your workflow smooth.
Storing Large Tool Outputs in Temporary Files
Managing large tool outputs is just as important as choosing which tools to load, especially when it comes to maintaining your AI’s context.
Some tools generate outputs that are simply too big to handle directly. Think about Chrome DevTools generating detailed network traces, a database query returning thousands of rows, or file system operations providing complete directory listings. If these massive outputs are fed directly into the AI, they can quickly use up tokens and divert attention away from the main tasks.
While auto-archiving these large responses could be a helpful solution, MCP Bundler doesn’t currently offer that feature. Instead, consider alternatives like pagination, server-side summarization, or providing a reference – such as a URI – to access the full dataset when needed.
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Monitoring Performance with Health Indicators
MCP Bundler’s Instant Health & Logs provides real-time insights into the performance of active servers, making it easier to quickly identify and address issues.
Identifying Tools That Consume the Most Context
With the Instant Health & Logs feature, you can monitor the status of active servers at a glance. The health indicators panel delivers a straightforward overview of key performance metrics, offering a snapshot of how your servers are functioning. This live data becomes the foundation for leveraging logs to fine-tune your server setup.
Using Logs to Optimize Configurations
Logs take this monitoring a step further by offering actionable data to refine your configurations. MCP Bundler’s logs are designed to pinpoint and resolve connection problems. By analyzing request logs, you can verify server operations and quickly address any irregularities. This proactive approach ensures your servers remain stable and perform efficiently.
Tips for Keeping Context Clean
When using MCP Bundler’s dynamic tool management, keeping your context streamlined is key. Here are some effective strategies to avoid unnecessary clutter.
Group Tools by Project Type
Organize your tools into bundles tailored to specific project types. This ensures you load only the tools you need, keeping your workflow efficient. Aim for compact collections of 3–6 essential tools per server to match your current tasks.
For example, if you’re working on web development, bundle tools like HTML extraction and page reading. For data analysis, group database and file processing tools. This way, you’re not bogged down by inactive tools consuming valuable context space.
A practical example comes from the misterboe/webtools-mcp-server on GitHub, as of August 2025. Users can configure the server to load specific tool presets via the ENABLED_TOOLS environment variable or the --tools CLI argument. Using the BASIC preset – which includes only gethtml and readpage – reduced token usage from approximately 10,300 tokens (for all 10 tools) to just 1,000 tokens. That’s an 89% reduction in context bloat.
For handling sensitive data, consider setting up dedicated servers with clearly labeled, essential tools. This approach minimizes unnecessary exposure and keeps your context lean.
Review and Remove Unused Configurations
Routinely auditing your servers and tool configurations is another effective way to cut down on clutter. Tools that were useful a few months ago might no longer be relevant as your projects evolve.
Automated flag management can help identify unused configurations, making it easier to remove outdated elements and reduce technical debt. Schedule monthly reviews to check which tools and servers are actually in use. Analyze your logs to pinpoint configurations that haven’t been accessed recently. If a tool hasn’t been called in weeks, consider deactivating it or moving it to a project-specific setup that you can enable when necessary.
By clearing out obsolete settings, you’ll create a more efficient system that’s ready to handle context-specific tasks without unnecessary baggage.
Load Tools Based on Current Context
One of the simplest ways to optimize your workflow is to load only the tools you need for the task at hand. Focus on your immediate requirements instead of preparing for hypothetical future needs.
Before starting a session, think about what tools are essential for your current task. For instance, if you’re debugging API responses, design tools are unnecessary. Similarly, if you’re writing documentation, there’s no need to load database query tools. MCP Bundler makes this process seamless with its one-click toggling feature, allowing you to quickly switch configurations as your focus shifts throughout the day.
Loading all available tools by default can quickly eat up valuable context space. By limiting your active tools to those required for specific tasks, you’ll significantly reduce token usage, leading to a leaner, more efficient workflow.
Conclusion
Managing context effectively becomes a breeze with MCP Bundler. This tool offers simple yet powerful features to keep your AI workflows efficient and free from unnecessary clutter that could take up valuable token space.
MCP Bundler helps streamline your setup by toggling only the tools and servers you actually need. It dynamically loads tools using search_tool and call_tool, while archiving large outputs to maintain a lean context.
By combining these strategies with thoughtful bundling, regular configuration reviews, and selecting tools based on specific tasks, you can significantly enhance efficiency. Grouping tools by project type ensures you’re working with relevant resources, not an overwhelming pile of options. Periodic reviews allow you to identify outdated configurations and remove them, while loading tools only when needed keeps your workspace organized and your AI responses quick.
This targeted approach not only simplifies workflows but also helps avoid potential output issues. These optimizations ensure smoother and uninterrupted AI operations.
With features like one-click toggling and automated management, MCP Bundler seamlessly integrates context optimization into your workflow, creating a cleaner, faster, and more efficient environment for AI development.
FAQs
How does MCP Bundler optimize AI performance and reduce token usage?
MCP Bundler improves AI efficiency by letting you activate only the servers or tools you need with just a few clicks. This streamlined approach ensures a cleaner context, cuts down on unnecessary data processing, and helps reduce token usage.
Additionally, it handles large tool outputs by saving them in temporary files. This keeps responses short and avoids hitting token limits. It’s especially handy for tools with tight output size restrictions, ensuring smooth AI interactions without sacrificing critical information.
How can custom tool bundles in MCP Bundler improve project efficiency?
Using custom tool bundles in MCP Bundler can simplify your workflow by letting you activate only the tools or servers you actually need for a particular project. This means less clutter and more focus on the tools that matter most – all with just a few clicks.
Handy features like search_tool and call_tool make it a breeze to find and use tools that fit your current needs. For larger outputs, MCP Bundler can store data in a temporary file, keeping your workspace tidy while giving you clear steps to access the file. This is especially helpful when dealing with tools that have strict limits on output size, ensuring your work stays efficient and easy to handle.
How do the search_tool and call_tool functions help optimize context and save tokens?
The search_tool function allows the AI to determine which tools are accessible and how to search within them based on the provided context. It also includes straightforward guidance on using each tool efficiently.
The call_tool function activates the chosen tool and returns accurate results. By working together, these functions simplify tool usage, minimize irrelevant data in the context, and help save tokens by narrowing down to only the most relevant information.