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.
Report
@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.
Report
@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.
The journey of building Strata has been one of constant iteration and discovery.
When we founded the company in March, Xiangkai and I had a simple but ambitious vision: make AI agents truly useful by giving them access to the tools they need. We spent two months diving deep into the MCP Server ecosystem, building and evaluating every integration we could get our hands on - GitHub, Slack, Jira, Gmail, Google Drive, and dozens more.
But with more users coming, here's where we learned our most valuable lesson: a linear approach doesn't scale. Different customers have wildly different needs. One integration covering all use cases means juggling countless tools, and external tools are infinite while context windows are not.
The "aha" moment came during a Waymo ride to an SF tech event. Xiangkai shared his vision for what would become Strata, and initially, we thought this progressive approach only on ONE integration. We immediately coded a prototype and were surprised - it covered most use cases beautifully. That success made us greedy. We wanted to add another layer, one more tool on top of all integration ecosystem. Amazingly, it still worked.
I'll admit, I was pushing for an immediate launch. But Xiangkai insisted on thorough evaluation to guarantee product quality, and that turned out to be our most correct decision. Our entire team spent almost another month making Strata mature and polished. Days and nights filled with debugging, tool optimization, and user experience refinements. We extensively evaluated with human evaluators and benchmarks because we knew quality would make or break us.
Today, we're proud to support over 20+ companies and thousands of developers who are building their AI agents and workflows with us. Every bug fix, every late night, every heated discussion about launch timing, it all led us to this moment where we can confidently say Strata brings real value to anyone building AI applications or agents.
The journey from that Waymo ride idea to supporting fellow YC companies has taught us that great products aren't just built - they're refined, tested, and perfected through countless iterations and unwavering commitment to quality.
Love how Strata guides the AI step-by-step instead of dumping every tool at once—makes so much sense! I’ve totally hit that “choice paralysis” wall building agents before. Is there an easy way to add custom integrations?
Hey @cruise_chen Thanks! yeah, Strata seamlessly integrates with any external MCP server, you can add your own MCP Server in the API parameter - https://docs.klavis.ai/api-reference/strata/create#body-external-servers, we'll also support add external in UI this week as well. which integration you'd like to add? your internal one or some existing public?
@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
Great to see your launch today! Well done and big congrats to Strata😊
That’s super helpful, especially for me! I’ve faced too many wrong tool calls in my products, and Strata can easily solve this problem. After watching its complete workflow, I’m confident it will. Congratulations to the Strata team on this launch!
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.
Strata
The journey of building Strata has been one of constant iteration and discovery.
When we founded the company in March, Xiangkai and I had a simple but ambitious vision: make AI agents truly useful by giving them access to the tools they need. We spent two months diving deep into the MCP Server ecosystem, building and evaluating every integration we could get our hands on - GitHub, Slack, Jira, Gmail, Google Drive, and dozens more.
But with more users coming, here's where we learned our most valuable lesson: a linear approach doesn't scale. Different customers have wildly different needs. One integration covering all use cases means juggling countless tools, and external tools are infinite while context windows are not.
The "aha" moment came during a Waymo ride to an SF tech event. Xiangkai shared his vision for what would become Strata, and initially, we thought this progressive approach only on ONE integration. We immediately coded a prototype and were surprised - it covered most use cases beautifully. That success made us greedy. We wanted to add another layer, one more tool on top of all integration ecosystem. Amazingly, it still worked.
I'll admit, I was pushing for an immediate launch. But Xiangkai insisted on thorough evaluation to guarantee product quality, and that turned out to be our most correct decision. Our entire team spent almost another month making Strata mature and polished. Days and nights filled with debugging, tool optimization, and user experience refinements. We extensively evaluated with human evaluators and benchmarks because we knew quality would make or break us.
Today, we're proud to support over 20+ companies and thousands of developers who are building their AI agents and workflows with us. Every bug fix, every late night, every heated discussion about launch timing, it all led us to this moment where we can confidently say Strata brings real value to anyone building AI applications or agents.
The journey from that Waymo ride idea to supporting fellow YC companies has taught us that great products aren't just built - they're refined, tested, and perfected through countless iterations and unwavering commitment to quality.
Agnes AI
Love how Strata guides the AI step-by-step instead of dumping every tool at once—makes so much sense! I’ve totally hit that “choice paralysis” wall building agents before. Is there an easy way to add custom integrations?
Strata
Hey @cruise_chen Thanks! yeah, Strata seamlessly integrates with any external MCP server, you can add your own MCP Server in the API parameter - https://docs.klavis.ai/api-reference/strata/create#body-external-servers, we'll also support add external in UI this week as well. which integration you'd like to add? your internal one or some existing public?
Den
Finally, the mother MCP
Strata
@justin_lee27 Thanks! the father Agent!
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!
Great to see your launch today! Well done and big congrats to Strata😊
Strata
@janette_szeto Thank you Janette!
That’s super helpful, especially for me! I’ve faced too many wrong tool calls in my products, and Strata can easily solve this problem. After watching its complete workflow, I’m confident it will. Congratulations to the Strata team on this launch!
Strata
@moeez_ur_rehman1 Thank you Moeez! happy to support!