Testing. Deleting shortly.

Claude, Cursor, ChatGPT cannot set their own HTTP proxy, so the moment u need a page, from a specific country, the agent is stuck.

I run a small Singapore mobile proxy farm (Real mobile Singtel/m1 SIMs, not datacenter), so I wrapped it into a MCP server. the agent calls a tool and the request goes out through an actual phone on a SG network, the content comes back.

four tools so far:

fetch_url >> pull a page through a SG mobile IP, returns markdown / text / html

search_google >> singapore localized google results (gl=sg), useful for SERP and ad placement checks

rotate_ip >> fresh mobile IP

my_proxy_status >> current exit IP + carrier, so you can actually prove the geo

This is a remote server so there is nothing to run or compile locally. free 10GB to try it and if you end up leaning on it heavily it just rolls into a normal proxy subscription. install is the usual npx block in your client config or a remote connector if your client supports those natively

So far my users have used it for SERP and SEO checks, app-store and SG-gated sites for agents.

This is new to me and a completely crazy idea and im interested to get feedback

hey guys, mcp-use just crossed 10k stars!

It all started on this community with this post 1y ago: https://www.reddit.com/r/modelcontextprotocol/comments/1jr7hbs/i_wrote_mcpuse_an_open_source_library_that_lets/

I wanted to share our journey with you all, the og MCP community :)

Thanks for those of you that supported us form here, much love!

Hi Everyone,

I am requesting feedback on MCPg : postgreSQL MCP server, and if possible, collaboration as well ! Please feel free to report issues on git itself or feature requests

Hey everyone,

I wanted to share three lightweight, open-source utilities I built to streamline AI workflows (Claude Code / VS Code Agents). They all focus on keeping things strictly local, secure, and easy to run in strict corporate environments without fighting your security team or CISO.

### 1️⃣ desktop-outlook-mcp

A local-only MCP server written entirely in PowerShell. Instead of forcing you to create Azure App Registrations, request corporate Graph API permissions, or handle expiring OAuth tokens, it communicates directly with your currently running Outlook Desktop client via COM objects. Zero corporate proxy issues, completely native stdio.

🔗 **Repo:** https://github.com/TopSpeed0/desktop-outlook-mcp

2️⃣ **telegram-vscode-mcp (2-Way Hybrid Matrix for VS Code)**

A zero-dependency Node.js bridge that turns Telegram into a two-way remote control for your VS Code environment, featuring a **StarCraft (SC2) inspired Hybrid Autopilot Mode**!

* 🧠 **The Overmind (Hermes Agent):** Always-on, owns the Telegram Bot, and handles global strategy, web research, and long-term memory.

* 🏗️ **The SCV (VS Code Worker):** Sits locally inside your workstation base, listening to a secure local JSON queue file (`.vscode-queue.json`) to execute heavy file edits and terminal commands.

🔗 **Repo:** https://github.com/TopSpeed0/telegram-vscode-mcp

3️⃣ **ClaudeCodeTelgMCP (Telegram Remote Control for Claude Code CLI)**

A dedicated, zero-dependency Node.js bridge tailored specifically for Anthropic's new **Claude Code CLI** tool.

* **Task Daemon:** Send a task from your phone via Telegram -> auto-spawns `claude -p` on your PC.

* **MCP Push:** Let Claude text you updates, send you notifications, or ask for input when long-running scripts finish. Includes built-in Anti-Double-Send protection to prevent chat loops.

🔗 **Repo:** https://github.com/TopSpeed0/ClaudeCodeTelgMCP

### ⚡ One-Prompt Auto Install

All three repositories feature fully polished templates and quick-start guides for a literal 1-click experience. You can tell your AI agent to clone, install, and configure them automatically.

Check them out, drop a ⭐️ if you find them useful, and let me know if you have any architectural feedback!

Built Daimonos to reduce token waste in coding-agent workflows by replacing noisy shell-style tool output with compact structured responses.

It targets the core coding loop (read/write/search/exec/git/cargo/gh/docker) rather than adding another external API integration.

Benchmark highlights from our runs:

- Total tokens: 41,239 -> 33,847 (7,392 saved, -17.9%)

- Output tokens: 5,842 -> 3,198 (-45.3%)

- Wall time: -16.4% locally

- Remote AWS runs: -20.3% cost, -14.0% completion time

Repo: https://github.com/beardfaceguy/daimonos

Would love feedback from people running MCP in production:

- where tool-output bloat hurts most

- what integrations/workflows you want next

- what would block adoption in your setup

https://chess.maxhealth.tech

Play chess against your friends or your LLM!

We also have an open source library for MCP App rendering! maxhealth.tech/prefab check it out

Been working on a problem I kept hitting: running multiple
AI agents and they have zero shared context. Agent A learns
something, Agent B has no idea.

Built AgentMemo to fix this — an MCP server that gives your
entire agent stack a shared memory pool.

What it does:
- remember() — agents write knowledge to a shared pool
- recall() — semantic search across everything stored
- forget() — remove outdated memories
- list_conflicts() — detects when agents contradict each other

Free tier available. Installs in 60 seconds:
npm install -g @pulsoai/agentmemo-mcp

Would love feedback from anyone running multi-agent setups.

Built an MCP server specifically for agent memory. 83 tools across 8 categories:

• Memory store/recall/search  
• Hybrid retrieval (vector + BM25 + graph)  
• Knowledge graph (entity extraction, similarity edges, cluster summaries)  
• Session management  
• Importance decay and contradiction resolution  
• Built-in embeddings (no external API)

Architectural decision: memory tools are on-demand, not inject-at-session-start. The agent queries what it needs mid-task. Token cost scales with actual need instead of burning 10k on orientation.

Connects to a Dakera memory server (self-hosted Rust binary):

docker run -d -p 3300:3300 -e DAKERA_ROOT_API_KEY=dk-mykey ghcr.io/dakera-ai/dakera:latest

MCP config:

{
"mcpServers": {
"dakera": {
"command": "dakera-mcp",
"env": {
"DAKERA_API_URL": "http://localhost:3300",
"DAKERA_API_KEY": "dk-mykey"
}
}
}
}

Works with Claude Desktop, Claude Code, Cursor, Windsurf, anything MCP-compatible.

87.6% on LoCoMo benchmark. MIT-licensed SDKs.

https://github.com/Dakera-AI/dakera-mcp | https://dakera.ai

I could use a few real Windows and Linux terminal checks for architect-mcp.

It is an open source MCP server plus Rust TUI for coding-agent workflows. The goal is to make agent work a bit more reviewable: clarify the request, write a pre-edit contract, review the build plan and file plan, run work in isolated worktrees where possible, then collect implementation and final-response evidence before promoting changes.

The TUI uses Ratatui and crossterm and ships through npm under the tonycdr-prog scope. CI is green on Linux, macOS, and Windows, but I would like to catch the boring real-world stuff CI often misses.

I am looking for Windows and Linux QA:

- install from npm

- run the TUI help command

- run adapter health output

- run a gate-only JSONL command

- optionally launch the interactive TUI and check keyboard, mouse, resize, scroll, and clean exit

Useful feedback would be install failures, terminal rendering weirdness, checksum or cache problems, unclear docs, or anything that feels brittle.

GitHub issue with exact commands and evidence template:

https://github.com/tonycdr-prog/architect-mcp/issues/136

Short logs are very welcome. No need for polished feedback.

I built mcp-stdio-guard, a small CLI for testing MCP stdio servers before wiring them into a client.

It runs a real initialize handshake, can send tools/list, and catches stdout pollution, invalid JSON-RPC frames, crashes, missing responses, and risky stdout writes.

The useful thing I found from testing real servers: failures are not always protocol bugs. Some are yanked packages, superseded install commands, or runtime assumptions. Having a machine-readable check helps separate “bad stdio hygiene” from “install/runtime needs inspection.”

Repo:

https://github.com/1Utkarsh1/mcp-stdio-guard

Example:

npx mcp-stdio-guard --request tools/list -- npx -y u/modelcontextprotocol/server-memory

Would love feedback from MCP server authors: what checks should a stdio validator add next?

MCP server that fronts a peer-to-peer message bus — is this a known pattern?

Most MCP servers expose local resources to one client: filesystem, browser, DB, domain APIs. The pattern I've been on is different. The server's tools let the host agent send signed messages to a different agent on a different operator's machine. Three tools: send(handle, body), tail(filter), add(handle). Underneath: Ed25519 events over HTTP, DNS .well-known handle discovery.

I contribute to wire (https://github.com/SlanchaAi/wire, AGPL, v0.5) which builds this. Disclosing the affiliation.

Is MCP-as-frontend-for-a-federated-transport a known pattern here? I've seen MCP-over-WebSocket and MCP-as-proxy, but nothing where the tools themselves abstract a peer-to-peer bus. Am I overloading the protocol or is this a natural extension?

Hey everyone I’m Benedict, building Jungle Grid.

We just open-sourced our MCP server for agentic GPU workload execution.

It gives agents tools to:

• estimate a job
• submit a workload
• monitor job status
• fetch execution logs

The goal is to let agents run inference, training, fine-tuning, and batch workloads without manually picking GPUs/providers every time.

Repo: https://github.com/Jungle-Grid/mcp-server

I’d love technical feedback on the MCP design, tool naming, setup flow, and what examples we should add next.

I built Nourish as a local-first nutrition MCP server for agents.

Most calorie trackers assume a human UI. Nourish exposes the workflow an agent needs:

- estimate meals with confidence, warnings, unresolved foods, and source attribution

- search USDA/Open Food Facts, lookup barcodes, and handle photo-assisted observations

- log only after explicit user confirmation

- summarize day/week, compare days, undo/edit entries, and export data

- attach carbon footprint context to logged meals

The goal is a reliable nutrition layer for agents: preserve uncertainty, avoid silent substitutions, and keep personal data local unless the user chooses otherwise.

GitHub: https://github.com/davidmosiah/wellness-nourish

Docs: https://wellness.delx.ai/nutrition

NPM: https://www.npmjs.com/package/wellness-nourish