Alternatives
Products that do what Open-Source tool for automatic MCP API on top of your database for LLMs does
We’re building an open-source tool that makes it easy to expose secure, LLM-optimized APIs on top of your structured data—without manually designing endpoints or worrying about compliance. AI agents and LLM-powered applications need structured access to data, but traditional APIs and databases weren’t built with AI workloads in mind. Our tool automatically generates APIs that: - Filter out PII & sensitive data to comply with GDPR, CPRA, SOC 2, and other regulations. - Provide traceability & auditing, so AI apps aren’t black boxes, and security teams stay in control. - Optimize for AI…
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- 9OS
Hello, my name is Andrei. My friends and I recently built CentralMind Getaway, an open-source tool that automatically generates AI-agent-optimized APIs from your database connection. It’s designed for those who don’t want to expose direct SQL access to their databases and prefer not to spend time building these APIs manually. What it does: - Auto-generates APIs from your database connection, infer schema & sample data using AI - Filters out PII and sensitive data for compliance (GDPR, SOC 2, etc.) - Optimized for AI-Agent with extra meta information and REST and MCP protocol support -…
2025 · github.com
- 10AC
Hi HN, we're Ashpreet, Eli and Yash and we're excited to share Phidata: a collection of AI Apps built with open-source tools. While helping teams build AI products, we built templates for spinning up LLM Apps quickly. Today we're open-sourcing our templates for building: - RAG LLM Apps - Autonomous LLM Apps - Multimodal LLM Apps - Data Engineering LLM Apps Templates are built with FastApi for serving, Streamlit for prototyping, PgVector for vectors and PosgreSQL for storage. Run them locally using docker and in production on AWS - with 1 command. - Github:…
2023 · github.com
- 11AP
As a former CIO who managed teams working with millions of lines of legacy code (Visual Basic, Sybase, Oracle Forms, and worse), I feel the pain of maintaining and onboarding developers to legacy systems. Believing that LLM-enabled tools can play a role in solving this, I've built a tool that automatically generates documentation for legacy codebases using the Model Context Protocol (MCP) & Claude Sonnet. At first glance, I think this approach has merit. Some samples are in the README. I welcome your thoughts. The Problem: - Legacy codebases are notoriously difficult to understand and…
2025 · github.com
- 12CL
With the right technique, I was able to break the so-called secure models like Claude and OpenAI. So, I built an open-source tool to automate this and find security holes in any hosted model. I got claude-sonnet-4 to demonstrate the following harmful behavior: - steal data from downstream tool calls using sql injection, code injection and template injection attacks - install spyware or malware using prompt obfuscation to send data to a third-party server Try it yourself with this simple command: pip install compliant-llm && compliant-llm dashboard
2025 · github.com
- 13LF
We built a no/low-code tool that lets you spin up MCPs from a single prompt. MCPs give LLMs access to tools, data, and actions—but they’re hard to build and deploy. Our tool abstracts that: describe what you want, and it auto-generates and hosts the necessary components. No UI flows, no manual chaining—just prompt and go. Examples: • Pull email, parse a DocSend, check Reddit, draft reply • Extract data from a niche site + send a Slack alert • Combine tools without writing glue code Live demo: https://www.youtube.com/watch?v=4uCiaQrgfoE Built over a weekend after getting…
2025 · generatemcp.com
- 14HL
At testup.io we have been working for a while to bring artificial intelligence to the field of test automation. Just a few years ago, the primary challenge laid in accurately identifying UI elements following minor structural changes, such as updates to IDs or paths. The emergence of Large Language Models (LLMs) raised the bar for what it meant to be smart. Now, we anticipate the robot to do lots of things autonomously, such as retry in cases of unresponsiveness or handle minor error reports. A more challenging, but soon expected feature, would involve the test robot navigating your web shop…
2024 · github.com
- 15IB
TLDR; I built a tool that turns any API into a CLI designed for ai agents --- Got tired of dealing with bloated context windows from MCP servers and skills that stuff entire API docs into the agent's context CLIs fix this, agents run a single command to self-discover everything an API has to offer So, built a tool to generate them for any api. All CLIs are written in Go, fast and lightweight, no dependencies Help text (via the --help flag) is the killer feature: all context for each command/endpoint/parameter is extracted directly from the user-facing API docs and enhanced with…
Mar 2026 · instantcli.com
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A few months ago I was working on a flight search engine that would include pet transport costs (I know a few by hearth but storing them and make the calculations in the UI would be nice) While I was collecting pet pricing from several airlines I strugled to extract data in a common format without hallucinated values. That's when I thought: What if I use multiple LLMs and take the most common response to improve accuracy? This idea became this new project. You provide your documents, an SQLModel schema, an LLM provider, plus what you'd like to extract and Extrai does the rest. Including…
Nov 2025 · github.com
- 18OS
Hi HN, Matvey, Ildar, Joey, and Dominik here. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools. We were too, and found that solutions like prompt-based filtering or secondary "guard" LLMs can be unreliable. Our thesis is that agent security should be handled at the network level between the agent and the LLM, just like a traditional web application firewall. So we built Archestra Platform: an open-source gateway that acts as a secure proxy for your AI agents. It's designed to be a deterministic firewall against…
Oct 2025 · archestra.ai
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Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…
2024 · github.com
- 20MS
For the past 10 years at Timescale (now Tiger Data), I've been been watching developers make mistakes in database design: forgetting to index foreign keys, creating case-sensitive email lookups, using arbitrary VARCHAR limits. And I've seen the pain and frustration when there is major downtime and botched migrations fixing things. AI coding could have made this better but is only making things worse. It's infuriating. So, I (along with my awesome team at Tiger Data) built an MCP server that auto-injects Postgres best practices into AI context to help. The biggest innovation: our…
Oct 2025 · github.com
- 21CA
Problem: LLMs need context data to give accurate answers and thousands of great publicly available datasets remain vastly unexploited. Anthropic’s MCP servers are awesome, but we need many more servers and an easy way to discover them. What if it took only one command to connect any LLM to any open dataset? Open Data MCP, a 2-in-1 solution: 1. Access Open Data: - 10 seconds setup to query any integrated Open Data MCP server from Claude (and more to come) - Simple CLI tool 2. Access Open Data: - Create your own Open Data MCP server(s) - Get templates, guides, and community - Get instant…
2024 · github.com
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We made human-use. Similar to how browser-use connects agents to the web, human-use connects agents to people all over the world in real-time using the Rapidata API. This allows the LLM to crowdsource human feedback and insights when it deems necessary. Free to use for anyone, you can enable your agent to use humans in real-time to: - Do preference research - Check for hallucinations - Capture sentiment - Get feedback - etc. Whatever you would want from humans. We expose certain parts of the Rapidata API to the agent through the MCP server framework. Additionally we provide a custom client.…
2025 · github.com
- 23AG
I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
- 24CS
We've been building with AI tools and noticed there wasn't a good way to manage MCP servers across a team or see what's actually flowing to LLM providers. Who's running what? Which tools are approved? What data is going where or whats shared on AI websites? So we built CyberCage (). What it does: MCP Management — Auto or manual discovery of MCP servers, with approval workflows. Manage allowed MCP servers org-wide (down to individual tools). Secure MCP catalog (integrates with GitHub's MCP Catalog). Operations — Manage allowed AI applications org-wide. Full audit logs (Splunk integration…
Dec 2025 · cybercage.io
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