Every processed story in chronological order, with the newest coverage first. Filter by tag, source, or score to drill in.
Weftly extends MCP-connected agents into video production workflows — clip extraction, transcription, and YouTube publishing — through a pay-per-job model that avoids subscription overhead.
mcp-gen removes the need to manually write MCP schemas by deriving them directly from TypeScript type definitions.
The project extends OpenRouter's Fusion Panel beyond its native interface by wrapping it as an MCP server, making it accessible to any MCP-compatible client.
The server provides a working diagram-generation path for Codex Desktop users who are blocked by the live-canvas timeout that prevents the official tldraw MCP App from functioning in that host.
The server gives AI models like Claude a standardized, structured path to YouTube's content layer — transcripts, metadata, and search — without requiring custom API integration work from the developer.
The project offers a path to running a large open-weight model for bulk agentic coding tasks without per-token API costs, rate limits, or third-party data exposure, by pairing MCP with rented decentralized GPU compute.
The server gives MCP-compatible AI clients a ready-made, no-credential bridge to live government weather data, covering both state-level emergency alerts and coordinate-based short-term forecasts.
SwitchAI makes Italian energy tariff data and bill analysis available as a zero-friction MCP tool, removing the need for authentication or custom integration to access live market indices and multi-offer comparisons.
Recall replaces ad-hoc agent memory approaches — full chat logs, vector indexes, or manually re-injected summaries — with a structured, self-updating graph that agents on multiple model families adopted autonomously without explicit prompting, removing the need to repeatedly re-inform agents of updated facts or resolved problems.
The proxy delivers simultaneous token cost reduction and accuracy improvement over plain JSON — without requiring any changes to existing MCP servers — by replacing a format that causes LLM comprehension failures at scale with one that scores 90.7% vs. JSON's 53.6% on the same data.