Launch HN: Strata (YC X25) – One MCP server for AI to handle thousands of tools
10 comments
·September 23, 2025tleyden5iwx
Looks really useful! Do you happen to have a gallery of apps using it? In particular, I'd like to see how desktop or mobile apps handle the oauth flows.
ssgodderidge
Is the goal to make a “universal MCP” that makes it easy to let MCP clients execute thousands of tools on a session by session basis? Or is it more focused on initial tool discovery and registration? If it’s the former, does the process add more latency between user taking action and tool getting executed?
wirehack
Yes it is the former. The value comes from its progressive guidance during a task, not just in the initial setup.
As for latency, we optimized for that. For examples, Strata automatically uses a direct, flat approach for simple cases. And we use less tokens compared to official MCP servers as well, as shown in the benchmark.
CuriouslyC
This is something I actually started to work on and put down because it wasn't exciting enough, but it's a legit product that fills a niche and congrats on the launch.
The biggest issue I found was getting agents to intelligently navigate the choose your own adventure of searching for the right tool. It amazes me that they're so good at coding when they're so bad at tool use in general. I'm sure your MCP responses were a fun bit of prompt engineering.
wirehack
Haha yeah we did optimize it a lot before the launch!
Actually for us, our first prototype was pretty good! We are also surprised about that because it took us a day or so to build the the prototype (only for one integrations though). Then it took us another week to build another prototype for multiple integrations.
justchad
This looks very relevant and useful to what I'm working on at the moment. The LLM gets lost in all of the tools we provide for certain actions.
wirehack
Glad it could be helpful to you! Curious what AI agents you are building and exactly what tools caused the failure.
In any case, feel free to give us a try!
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bigolnik
How does this differ from something like nexusrouter?
wirehack
As you can see from our examples, the main approach is not tool search. Instead, Strata guides your AI agent step by step, going from server to categories to actions to action details so that the model does not get overloaded. We actually have 1000+ tools for some of the integrations (e.g. GitHub) and this approach works better than traditional methods.
Think of it as a search engine vs. a file explorer. But we do provide documentation search as well. So you get the best out of the two worlds.
Hey HN! We are Klavis AI (https://www.klavis.ai/) and we're launching Strata, one open-source MCP server that helps AI agents use thousands of API tools without getting overwhelmed. Instead of showing all available tools at once, Strata reveals them step-by-step based on what the AI actually needs.
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: (1) AI agents struggle to pick the right API from hundreds of options. (2) Tool descriptions and info consume massive token budgets. (3) Most servers cap at 40~50 tools to avoid these problems, limiting what you can build.
Instead of flooding the AI with everything upfront, Strata works like a human would. It guides the AI agents to discover relevant categories, then lists available actions in those categories. It relies on LLMs’ reasoning to drill down progressively to find the exact tool needed. Here are some examples:
Github query: "Find my stale pull requests in our main repo"
Strata: AI model identifies GitHub → Shows categories (Repos, Issues, PRs, Actions) → AI selects PRs → Shows PR-specific actions -> AI selects list_pull_requests → Shows list_pull_requests details -> Executes list_pull_requests with the right parameters.
Jira query: "Create a bug ticket in the 'MOBILE' project about the app crashing on startup."
Strata: AI identifies Jira → Shows categories (Projects, Issues, Sprints) → AI selects Issues → Shows actions (create_issue, get_issue) → AI selects create_issue → Shows create_issue details → Executes with correct parameters.
Slack query: "Post a message in the #announcements channel that bonus will be paid out next Friday."
Strata: AI identifies Slack → Shows categories (Channels, Messages, Users) → AI selects Messages → Shows actions (send_message, schedule_message) → AI selects send_message → Shows send_message details → Executes with correct parameters.
This progressive approach unlocks a huge advantage: depth. While most integrations offer a handful of high-level tools, Strata can expose hundreds of granular features for a single app like GitHub, Jira, etc. Your AI agent can finally access the deep, specific features that real workflows require, without getting lost in a sea of options.
Under the hood, Strata manages authentication tokens and includes a built-in search tool for the agent to dig into documentation if it gets stuck.
On the MCPMark https://mcpmark.ai/leaderboard/mcp, 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 multi-app workflows.
Here is a quick demo to watch Strata navigate a complex workflow with multiple apps, automatically selecting the right tools at each step: https://www.youtube.com/watch?v=N00cY9Ov_fM.
You can connect to any external MCP Server into Strata, and we have an open source version for it: https://github.com/Klavis-AI/klavis.
For team or production use with more features, visit our website: https://www.klavis.ai. Add Strata to Cursor, VS Code or any MCP-compatible application with one click. You can also use our API to easily plug in Strata to your AI application.
We look forward to your comments. Thanks for reading!