Alternatives
Products that do what Wardn Hub does
Publish, discover, and evaluate MCP servers and agent skills
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Hi HN, We’re building security tooling around agentic AI systems. Today, we're releasing our public MCP catalog with detailed risk analysis for every MCP server we've found on the internet: https://mcp.armor1.ai/mcp-directory We all love agents and the power that MCPs unlock: suddenly your AI assistant can query databases, manage files, call APIs, and interact with the real world. But when we started adopting MCPs ourselves, we kept running into the same nagging questions: Is this MCP safe? Where is my data actually going? Could it execute destructive actions? Is it…
Feb 2026 · mcp.armor1.ai
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Hey HN, I built pg-mcp, a Model Context Protocol (MCP) server for PostgreSQL that provides structured schema inspection and query execution for LLMs and agents. It's multi-tenant and runs over HTTP/SSE (not stdio) Features - Supports multiple database connections from multiple agents - Schema Introspection: Returns table structures, types, indexes and constraints; enriched with descriptions from pg_catalog. (for well documented databases) - Read-Only Queries: Controlled execution of queries via MCP. - EXPLAIN Tool: Helps smart agents optimize queries before execution. - Extension…
2025 · github.com
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Hey HN, I spent my xmas break building an agent framework called mcp-agent [1](https://github.com/lastmile-ai/mcp-agent) for Model Context Protocol [2]. It makes it easy to build AI apps with MCP servers, and implements every pattern from the popular Building Effective Agents blog [3] as well as OpenAI’s Swarm [4]. I’m sharing it early to get community feedback on where to take it from here, and to ask for contributions. For those who aren’t familiar with MCP, I think of it as a standardized interface to let AI communicate with software via tool calls, resources and…
2025 · github.com
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Jul 2026 · github.com
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Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…
2025
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https://github.com/mcp-shark/mcp-shark Site: https://mcpshark.sh/ I built MCPShark, a traffic inspector for the Model Context Protocol (MCP). It sits between your editor/LLM client and MCP servers so you can: • See all MCP traffic (requests, responses, tools, resources) in one place • Debug sessions when tools don’t behave as expected • Optionally run “Smart Scan” checks to flag risky tools / configs
Dec 2025
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2025 · github.com
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Hey HN! A few months ago we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework that implements every agent pattern from Anthropic’s Building Effective Agents blog [3] and handles MCP server/client management seamlessly. Our core bet is that connecting LLMs to tools, resources, and external systems will soon be MCP-native by default. Today we're launching a significant update: Agents as MCP servers. Currently "agentic" behavior exists only on the MCP client side – clients like Claude or Cursor use MCP servers to solve tasks.…
2025 · github.com
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2025 · github.com
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Built this to track the ongoing debate around Model Context Protocol - whether it's gaining real traction or just hype. Pulls live data from GitHub, HN, Reddit and a few other sources. Curious what the HN crowd thinks given how active the MCP discussion has been here.
Apr 2026 · ismcpdead.com
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I've worked at several companies during the past two decades and I kept encountering the same issues with internal technical proposals: - Authors would change a spec after I started writing code - It's hard to find what proposals would benefit from my review - It's hard to find the right person to review my proposals - It's not always obvious if a proposal has reached consensus (e.g. buried comments) - I'm not notified if a proposal I approved is now ready to be worked on And that's just scratching the surface. The most popular solutions (like Notion or Google Drive + Docs) mostly lack…
Dec 2025 · rfchub.app
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2025 · github.com
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Hi HN! We’re Theodore and Louis, founders of Armature (YC P26). We reconstruct the entire session behind the MCP tool calls you receive, including what the user asked their agent to do and what the agent thought. You wrap your MCP in 3 lines of code (our SDK is available in Typescript, Python and Go) and start seeing in your dashboard: - All sessions reconstructed: it’s like reading the real conversation the user had inside Claude or ChatGPT! - A ranking of your MCP most popular use cases, built from sessions clustering - The most frequent issues your users’ agents encounter so you can fix…
Aug 2026 · armature.tech
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Most of the MCP servers that I’ve seen are tools implemented in standalone projects. To onboard more tools (especially agents and multi-agent workflows) to MCP, I’ve been thinking it’s important to allow AI engineers to continue to prototype in their existing agent frameworks and deploy with minimal conversion when ready. We created the automcp library, which you can add as a dependency to existing projects (CrewAI, LangGraph, Llama Index, OpenAI Agents SDK, Pydantic AI, mcp-agent currently supported but more coming soon). You just need to run a CLI command to create a run_mcp.py file, make…
2025 · github.com
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Got tired of every MCP example being overly verbose, or needing Docker or Mac-only scripts, so I threw together MCP-123. Point it at a tools.py, run `server.run_server(...)`, and the client auto-discovers/calls functions with OpenAI. I hope this is useful to you all.
2025 · github.com
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I started this project because I believe MCP has the potential to transform how AI models interact with external resources, but the ecosystem is still very fragmented. There's no central place to discover and compare all the server implementations available, which makes adoption and experimentation harder for developers. I initially built the GitHub repo awesome-mcp-servers (https://github.com/punkpeye/awesome-mcp-servers/), and it's been amazing to see the community contribute to it. Now, I'm taking it further with a directory that automates many tasks—introspecting…
2024 · glama.ai
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