The Model Context Protocol (MCP) standardizes how AI applications connect to tools and data, but it fails to solve the problem of tool selection when systems scale to dozens or hundreds of tools. As A…
Local MCP servers enable individual developers to quickly connect AI clients to tools and data, but teams face significant friction when multiple members need to use the same setup. The local approach…
The shift from prompt engineering to tool engineering is now required as AI workflows move beyond simple conversation into task execution. Prompt engineering alone fails when models need access to ext…
LLMs can perform tasks like writing code and summarizing documents, but developers face a choice between giving the model a "skill" to improve its internal behavior or connecting it to an MCP server f…
MCP servers in production require observability through logs, metrics, and replayable context because AI tool calls introduce debugging challenges that traditional API monitoring does not address. Wit…
AI coding assistants like Claude Code, Cursor, and Windsurf frequently forget project architecture, conventions, and rules, forcing developers to repeatedly re-explain context. A centralized knowledge…
Developers building AI-generated applications are moving integration logic from client-side code to MCP tools to reduce brittleness and complexity. By exposing backend capabilities as named tools thro…
Vectoralix launched a SaaS platform for hosting and managing MCP servers, introducing versioning, categorization, and access control to solve lifecycle challenges in production AI workflows. The platf…
Vectoralix launched a new platform that converts Git repositories into hosted MCP (Model Context Protocol) servers, eliminating the need for developers to build custom protocol handling, authenticatio…
Vectoralix has introduced a managed gateway platform for securing remote Model Context Protocol (MCP) servers, implementing OAuth, bearer tokens, and scoped permissions to authenticate and authorize A…