Strata is one MCP server that guides your AI agents use tools reliably at any scale in multiple apps progressively. It eliminates context overload and ensures accurate tool selection, enabling agents to handle complex, multi-app workflows with ease.
Hey Product Hunt! 👋 We're Klavis AI and are launching Strata, one MCP server for AI agents to use tools reliably at any scale progressively. As a former Senior SWE on Google @Gemini 's tool use team, I saw firsthand how AI would struggle with tools. If you've built AI agents, you've likely hit the same walls: 🤯 Tool Overload: Too many tools cause AI "choice paralysis."
💥 Context Overload: Long tool lists blow up token counts and costs.
📉 Coverage Gap: Most servers are stuck at <40~50 tools, limiting what you can build.
How Strata works
Strata fixes this. Instead of overwhelming the AI, it guides it. ✨Think of it as a smart layer that helps the AI think like a human 🧠. For a query like "find my leads in hubspot," Strata guides the AI through a logical flow:
1️⃣ Intent → Integration: First, it identifies the user who wants to use @hubspot .
2️⃣ Integration → Categories: It then shows available categories like "Accounts," "Campaigns," and "Leads."
3️⃣ Category → Actions: The AI drills down into "Leads" and finds relevant actions like "find_lead."
4️⃣ Action → Execute: Finally, Strata pulls the specific API details and runs it. ✅
Results
This approach delivers real results 📊. On the MCPMark benchmark, Strata achieves +15.2% higher pass@1 rate vs the official GitHub server and +13.4% higher pass@1 rate vs the official Notion server. In human eval tests, it hits 83%+ accuracy on complex, real-world workflows.
Join us! Ready to build powerful, multi-app AI agents with Strata? 👉 Sign up for free! Want to chat? Email us: founders@klavis.ai or ☎️ Book a call. Strata is also open-source! Check out the code, contribute, or self-host.
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@xiangkai_zeng this looks awesome, solves a huge pain point.
@mcval_osborne Thank you! Glad you like it! What AI Agents are you building?
Report
@xiangkai_zeng several things! but specifically now an ai agent which reviews tweets on X and posts on LinkedIn and recommends relevant replies to your target ICP.
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@hubspot@xiangkai_zeng really cool approach! Lowe how Strata tackles tool overload with that step-by-step flow. Curious though how easy is it to add custom integration and does the extra routing add any latency? excited to see where this goes
@hubspot@xiangkai_zeng The hierarchical tool discovery approach addresses a real scalability problem with MCP servers. The progressive intent → integration → category → action flow makes sense for reducing cognitive load on the model.
The benchmark improvements over official GitHub and Notion servers are solid, but those baseline servers weren't really designed for handling thousands of tools. How does Strata perform against other purpose-built tool orchestration systems rather than just basic MCP implementations?
The Google Gemini tool use team background gives credibility to understanding the core problem. What's the latency overhead for the multi-step routing process compared to direct tool access, especially for simple queries that don't need the full hierarchy?
@alex_chu821 You've perfectly described the benefit of reducing the model's cognitive load.
On the benchmarks, that's a great point. The benchmark eval cases are actually designed a single app's toolset (like just Notion), where a basic server should shine. (i.e. It is not a benchmark designed to test thousands of tools). Our Strata guided approach is still more accurate even without the complex multiple apps scenarios.
As for latency, we optimized for that. For simple queries, we optimized Strata so that it automatically uses a direct, flat approach for simple cases to ensure there’s no overhead. And we use less tokens compared to official MCP servers as well, as shown in the benchmark.
@gabe Please feel free to give Strata a try! Strata guides your AI agents to handle thousands of tools progressively. And our eval results show significant improvements compared to traditional approaches. https://mcpmark.ai/leaderboard/mcp
+1 Rube is really impressive! @gabe good question!
Strata uses the model’s natural reasoning to navigate tools step by step, just like a human would, layer by layer, rather than relying on semantic search.
Strata is available as a UI, API, and also open source.
As Xiangkai mentioned, we’re also showcasing our evaluation results
You can check out blog for more details of Strata design!
Report
Can you clarify what “thousands of tools” means in practice? Are these third-party integrations, or do you allow custom user-built tools?
@aboubacar_sissoko good question! both! it includes tools within 3rd party integration but also compatible with external integration! curious what's your use case?
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When you say context overload, how does Strata manage the token / context window limits — do you dynamically prune tools or cache metadata?
@tenin_traore Great question! It's a combination of both. At each step of the flow (Integration -> Category -> Action), we only load the relevant data for that specific level. So instead of putting thousands of tools in the context, the model only ever sees a handful of relevant options. We also cache this metadata heavily to keep things fast. It dramatically reduces the token count per call.
Report
I’m building an Multi-Agent Launch Orchestrator—a system where 15 specialized AI agents work together to automate from market research and requirement synthesis to launch comms and post-launch analysis. Each agent handles a specific task and passes outputs to the next, with a real-time dashboard, API endpoints, OpenRouter/OpenAI/LLM support, and a modern React frontend for workflow tracking.
Given that, I’m curious—how does Strata handle these kinds of sequential, multi-agent flows where passing context is key? Does it support built-in agent chaining, real-time progress tracking, or consolidated reporting the way an orchestrator would? Would love to know if Strata is designed for these advanced, cross-agent use cases or if you see any best practices!
@sneh_shah That's a seriously powerful and impressive system you're building! It's a perfect example of the advanced use cases we're excited about.
Here’s how Strata fits in:
Your Launch Orchestrator is the "brain" that handles the high-level agent chaining, context passing, and progress tracking. Strata acts as the specialized "hands" for each of your 15 agents.
Think of it this way:
Your Orchestrator decides: "Okay, Agent 3 (Comms) now needs to post to Slack and Gmail."
Agent 3 uses Strata as the MCP integration layer.
Strata guides the agent: It helps Agent 3 find the precise gmail.send_email and slack.post_message tools out of potentially thousands of other tools and apps, without you needing to stuff those details into its context.
So, while Strata doesn't handle the agent-to-agent orchestration itself, it's the critical execution layer that makes each agent in your chain more reliable, scalable, and cost-effective. The best practice is to pair a system like yours with Strata to get the best of both worlds.
Report
The benchmark results against the official GitHub and Notion servers are quite impressive! Would you mind sharing a bit more about the methodology of the MCP Mark benchmark? I'm curious about the types of complex workflows you tested.
@emily_tian Thanks for digging into the data! We're actually planning to release a more detailed blog post about the MCPMark benchmark methodology next week. In short, it involves a series of multi-step, multi-app tasks designed to mimic real-world user requests. Happy to share the link with you when it's live!
Congrats on the launch! This is a brilliant solution to the tool overload problem for AI agents. The progressive disclosure approach is super smart. Quick question: Can Strata learn from user interactions to eventually optimize these pathways or suggest common action sequences? 🤔
Strata
Hey Product Hunt! 👋
We're Klavis AI and are launching Strata, one MCP server for AI agents to use tools reliably at any scale progressively. As a former Senior SWE on Google @Gemini 's tool use team, I saw firsthand how AI would struggle with tools. If you've built AI agents, you've likely hit the same walls:
🤯 Tool Overload: Too many tools cause AI "choice paralysis."
💥 Context Overload: Long tool lists blow up token counts and costs.
📉 Coverage Gap: Most servers are stuck at <40~50 tools, limiting what you can build.
How Strata works
Strata fixes this. Instead of overwhelming the AI, it guides it. ✨Think of it as a smart layer that helps the AI think like a human 🧠. For a query like "find my leads in hubspot," Strata guides the AI through a logical flow:
1️⃣ Intent → Integration: First, it identifies the user who wants to use @hubspot .
2️⃣ Integration → Categories: It then shows available categories like "Accounts," "Campaigns," and "Leads."
3️⃣ Category → Actions: The AI drills down into "Leads" and finds relevant actions like "find_lead."
4️⃣ Action → Execute: Finally, Strata pulls the specific API details and runs it. ✅
Results
This approach delivers real results 📊. On the MCPMark benchmark, Strata achieves +15.2% higher pass@1 rate vs the official GitHub server and +13.4% higher pass@1 rate vs the official Notion server. In human eval tests, it hits 83%+ accuracy on complex, real-world workflows.
Join us!
Ready to build powerful, multi-app AI agents with Strata? 👉 Sign up for free! Want to chat? Email us: founders@klavis.ai or ☎️ Book a call. Strata is also open-source! Check out the code, contribute, or self-host.
@xiangkai_zeng this looks awesome, solves a huge pain point.
Strata
@mcval_osborne Thank you! Glad you like it! What AI Agents are you building?
@xiangkai_zeng several things! but specifically now an ai agent which reviews tweets on X and posts on LinkedIn and recommends relevant replies to your target ICP.
@hubspot @xiangkai_zeng really cool approach! Lowe how Strata tackles tool overload with that step-by-step flow. Curious though how easy is it to add custom integration and does the extra routing add any latency? excited to see where this goes
Strata
@hubspot @adsapozhnikov Thank you! We support custom MCP servers in our API: https://docs.klavis.ai/api-reference/strata/create#body-external-servers. And based on our testing, the number of tokens is similar or smaller compared to traditional approaches. For more details, you can checkout our blog https://www.klavis.ai/blog/introducing-strata-one-mcp-server-for-thousands-of-tools.
@xiangkai_zeng Thanks for building this! Alleviating context overload from long tool lists is extremely valuable.
Strata
@mario_uccello Thank you for the kind words!
Scrumball
@hubspot @xiangkai_zeng The hierarchical tool discovery approach addresses a real scalability problem with MCP servers. The progressive intent → integration → category → action flow makes sense for reducing cognitive load on the model.
The benchmark improvements over official GitHub and Notion servers are solid, but those baseline servers weren't really designed for handling thousands of tools. How does Strata perform against other purpose-built tool orchestration systems rather than just basic MCP implementations?
The Google Gemini tool use team background gives credibility to understanding the core problem. What's the latency overhead for the multi-step routing process compared to direct tool access, especially for simple queries that don't need the full hierarchy?
Strata
@alex_chu821 You've perfectly described the benefit of reducing the model's cognitive load.
On the benchmarks, that's a great point. The benchmark eval cases are actually designed a single app's toolset (like just Notion), where a basic server should shine. (i.e. It is not a benchmark designed to test thousands of tools). Our Strata guided approach is still more accurate even without the complex multiple apps scenarios.
As for latency, we optimized for that. For simple queries, we optimized Strata so that it automatically uses a direct, flat approach for simple cases to ensure there’s no overhead. And we use less tokens compared to official MCP servers as well, as shown in the benchmark.
Product Hunt
Congrats on the launch @xiangkai_zeng and team! I LOVE the vision behind this. I'm curious how Strata compares to something like @Rube?
I'm currently using Rube for most of my integrations but am very excited to try Klavis out!
Strata
@gabe Please feel free to give Strata a try! Strata guides your AI agents to handle thousands of tools progressively. And our eval results show significant improvements compared to traditional approaches. https://mcpmark.ai/leaderboard/mcp
Strata
+1 Rube is really impressive!
@gabe good question!
Strata uses the model’s natural reasoning to navigate tools step by step, just like a human would, layer by layer, rather than relying on semantic search.
Strata is available as a UI, API, and also open source.
As Xiangkai mentioned, we’re also showcasing our evaluation results
You can check out blog for more details of Strata design!
Can you clarify what “thousands of tools” means in practice? Are these third-party integrations, or do you allow custom user-built tools?
Strata
@aboubacar_sissoko good question! both! it includes tools within 3rd party integration but also compatible with external integration! curious what's your use case?
When you say context overload, how does Strata manage the token / context window limits — do you dynamically prune tools or cache metadata?
Strata
@tenin_traore Great question! It's a combination of both. At each step of the flow (Integration -> Category -> Action), we only load the relevant data for that specific level. So instead of putting thousands of tools in the context, the model only ever sees a handful of relevant options. We also cache this metadata heavily to keep things fast. It dramatically reduces the token count per call.
I’m building an Multi-Agent Launch Orchestrator—a system where 15 specialized AI agents work together to automate from market research and requirement synthesis to launch comms and post-launch analysis. Each agent handles a specific task and passes outputs to the next, with a real-time dashboard, API endpoints, OpenRouter/OpenAI/LLM support, and a modern React frontend for workflow tracking.
Given that, I’m curious—how does Strata handle these kinds of sequential, multi-agent flows where passing context is key? Does it support built-in agent chaining, real-time progress tracking, or consolidated reporting the way an orchestrator would? Would love to know if Strata is designed for these advanced, cross-agent use cases or if you see any best practices!
Strata
@sneh_shah That's a seriously powerful and impressive system you're building! It's a perfect example of the advanced use cases we're excited about.
Here’s how Strata fits in:
Your Launch Orchestrator is the "brain" that handles the high-level agent chaining, context passing, and progress tracking. Strata acts as the specialized "hands" for each of your 15 agents.
Think of it this way:
Your Orchestrator decides: "Okay, Agent 3 (Comms) now needs to post to Slack and Gmail."
Agent 3 uses Strata as the MCP integration layer.
Strata guides the agent: It helps Agent 3 find the precise gmail.send_email and slack.post_message tools out of potentially thousands of other tools and apps, without you needing to stuff those details into its context.
So, while Strata doesn't handle the agent-to-agent orchestration itself, it's the critical execution layer that makes each agent in your chain more reliable, scalable, and cost-effective. The best practice is to pair a system like yours with Strata to get the best of both worlds.
The benchmark results against the official GitHub and Notion servers are quite impressive!
Would you mind sharing a bit more about the methodology of the MCP Mark benchmark? I'm curious about the types of complex workflows you tested.
Strata
@emily_tian Thanks for digging into the data! We're actually planning to release a more detailed blog post about the MCPMark benchmark methodology next week. In short, it involves a series of multi-step, multi-app tasks designed to mimic real-world user requests. Happy to share the link with you when it's live!
remio - Your Personal ChatGPT
Congrats on the launch! This is a brilliant solution to the tool overload problem for AI agents. The progressive disclosure approach is super smart. Quick question: Can Strata learn from user interactions to eventually optimize these pathways or suggest common action sequences? 🤔
Strata
@lvyanghuang Yes this is something we are working on! I think this is a very promising idea and happy to chat with you about it!