klavis

able 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!

Notion, Jira, Linear, Salesforce, etc.); Lightweight Alpine-based containers; Support for both Klavis-AI OAuth service or BYO tokens; CI/CD ready with automated updates via watchtower; Distributed via GitHub Packages (ghcr.io) GitHub: https://github.com/Klavis-AI/klavis Youtube video demo: https://www.youtube.com/watch?v=NITgggPT3pA Hosted versions: https://www.klavis.ai One more thing: As AI agents require more tools, they face limitations in tool selection, context window size, and understanding human context. We are building a solution to overcome these constraints, enabling agents to access any tool they need via MCP servers. Join our waitlist to be the first to know when we launch it. https://www.klavis.ai/waitlist

via our API. The API also provides built-in OAuth and multi-tenancy auth support for MCP servers. We also want to remove the need for developers to write MCP client code. You can use our API to interact with any remote MCP servers directly from your existing backend infrastructure. For faster prototyping or direct user interaction, we also provide open-source client interfaces for Web, Slack, and Discord. The MCP servers and clients code is open source because we want to contribute to the MCP community. For a quick start in the hosted verions, log in to our website and generate an API key. Then start calling our APIs directly. You can find more details in our doc: https://docs.klavis.ai For a quick start in the open source version, go to our github repository and check out the detailed readme on each MCP server and client. A little note about myself: my background includes working on the function calling for Google Gemini. During that time, I saw firsthand the challenges teams face when trying to connect AI agents to external tools. I want to bring my insights and energy to accelerate MCP adoption. This is an early release, and we’d appreciate feedback from the community. What are your worst pain points related to MCPs, either as a developer or a general user? What other MCP servers or features would be most valuable to you? We'll be around in the comments. Thanks for reading!

vis ReportGen (LLM + Web Search): This is a very cool one. Check out the report generated by the server with a simple query “generate a report about national parks in utah” https://www.klavis.ai/generated-reports/c150d577-ab66-45e4-9... - YouTube video - Document Conversion (via Pandoc) - And we modified various servers to support SSE. How to Try It Out: - We have our hosted version in www.klavis.ai, which has a UI to directly configure MCP clients and servers. - The github repo also has detailed instructions for local development and testing. The basic steps involve setting up a client (Discord or Slack) and running one or more MCP servers (like Report Generation). You can check the readme under mcp_clients and mcp_servers folder for details. - For those of you who do not have much time, check out our launch video: https://www.youtube.com/watch?v=9-QQAhrQWw8 This is an early release, and I'd appreciate feedback from the HN community: - What other tool servers (MCP endpoints) or client platforms would be most valuable? - Any thoughts on improving the developer experience for adding new servers/clients? - Discord for feedback: https://discord.gg/P6fFgv2w We'll be around to answer questions. Thanks for looking!