I will not promote - What cross-server authorization problems are you hitting with MCP?

Reddit r/AI_Agents News
mcp authorization security ai-agents permissions cross-server protocol

Summary

The article asks about cross-server authorization challenges when multiple MCP servers (e.g., Gmail, Github, Slack) are used together in an AI agent session, and whether a dedicated authz layer is needed beyond per-server OAuth.

Researching a real problem vs. a hypothetical one. Not pitching anything. If your agent has multiple MCP servers wired up in a single session like Gmail + Github + Slack. What are some toxic combinations and how are you keep your agents in check? Eg. an agent that has access to slack and github MCP. How are you ensuring that your agent doesn't leak private git repo code to public slack channel? Specifically curious about: * Tool combinations that are individually safe but dangerous together * How you're scoping permissions today (per-user, per-session, per-tool, nothing) Open to comments or DMs. Trying to figure out if MCP needs a dedicated authz layer between client and servers, or if per-server OAuth + client-side approval is enough.
Original Article

Similar Articles

GetMCP: Zero Trust for AI agents

Reddit r/AI_Agents

GetMCP is a self-hostable open-source tool that brings zero-trust security to AI agents by providing per-request audit, per-agent revocation, policy enforcement, and human-in-the-loop approvals for API calls. It generates MCP servers from OpenAPI specs and acts as a streaming proxy with tamper-evident audit logs.

How are you handling cross-client communication between MCP agents?

Reddit r/AI_Agents

A developer discusses the challenge of coordinating multiple MCP-speaking AI agents (like Claude Code and Cursor) working on the same project, sharing their self-built open-source solution using a shared 'room' model inspired by IRC, and asking the community for patterns and opinions.

MCP Hello Page

Hacker News Top

The author describes a common user onboarding problem with MCP servers—users opening the endpoint in a browser and seeing a 401 error—and shares a simple hack: returning an HTML page that explains how to properly add the server to an LLM client, which drastically reduced support tickets.

Code execution with MCP: Building more efficient agents

Anthropic Engineering

This article from Anthropic explores how integrating code execution with the Model Context Protocol (MCP) can improve the efficiency of AI agents. It addresses challenges like token overload from tool definitions and intermediate results, proposing code execution as a solution to reduce latency and costs.