There are already some interesting projects around MCP tooling and conversion layers like mcporter and similar libraries.
While trying them, I realized what I personally missed wasn’t just “wrapping” MCP servers, but having an environment where:
MCP tools become normal CLIs
they work naturally with pipes/scripts/CI
agents can use them without loading huge schemas every session
and you can also create your own CLI tools directly from Python code
So I started building cli-use.

Example:

cli-use add fs /tmp
cli-use fs list_directory --path /tmp

After that the MCP server behaves like a regular Unix command:

cli-use fs search_files --path /tmp --pattern "*.md" | head

also added things like:
daemon mode for fast repeated calls
caching
shell completions
automatic SKILL.md generation for agents

One thing I found interesting is that reducing all the MCP protocol overhead ended up saving a pretty large amount of tokens during agent workflows.
Still experimenting with the idea, but I’m curious whether other people working with MCP also want a more shell-native / Unix-style approach to tools.

Is there a free MCP server or paid tool that can connect Google Ads, Google Merchant Centre, Google Search Console, GA4, and hopefully WordPress/WooCommerce into one place for Claude or chatgpt to audit?

I am new to MCPs and have tried searching, but I mostly find separate tools like n8n, Supermetrics, Markifact and Zapier MCP. I am looking for something easier and more all-in-one, mainly for SEO, Ads and Merchant Center auditing, with manual implementation after.

Has anyone set this up or can recommend the best option? I am dont currently have a lot to spend so trying to keep costing down

Hi everyone,

I’ve been diving deep into how AIs interact with tools and quickly hit a wall with the Model Context Protocol (MCP). As soon as you build complex, real-world toolsets, MCP becomes inefficient—bloating the context window and killing performance.

To solve this, I’ve developed Elemm (Every Landmark Enables Massive Modularity), also known as "The Landmark Manifest Protocol."

👉 GitHub:https://github.com/v3rm1ll1on/elemm

Check out the docs and the benchmarks on GitHub.

MCP Classic vs Elemm - Model: GPT-OSS-120B - 111 Available Tools

What Elemm enables:

• Custom Tooling: Turn any Python function into a "Landmark" with a single decorator.
• Instant API Integration: Point to an OpenAPI or GraphQL URL, and your agent navigates it instantly with surgical precision.
• Seamless Migration: Easily bridge your existing tools into a manifest-driven architecture.

The Landmark Advantage

Elemm doesn't cram every tool definition into the prompt. Instead, it provides the agent with a dynamic Manifest File for safe, "lazy-loaded" navigation.

The Benchmarks:

• Scale: I gave an agent access to 845 tools simultaneously (GitHub API) with minimal token usage and 100% success rate on flagship models (Claude, Gemini, GPT-4).
• Efficiency: Compared to classic MCP, Elemm shows -92% token savings and -84% fewer steps.
• Edge Performance: Even using a tiny "goldfish-brain" model (Qwen 3.5 0.8B), I solved a multi-step forensic audit involving 111 tools with a 70% success rate. Standard MCP typically fails at the first step in this scenario.

Core Gateway Features:

• Universal Gateway: A built-in bridge for OpenAPI, GraphQL, and native Elemm services via MCP.
• On-Demand Discovery: Agents only load the definitions they actually need, preventing context overflow.
• Sequence Engine: Execute multiple API calls in a single turn with native data piping (Output A → Input B).
• Guardian Security: A policy engine that blocks dangerous patterns (e.g., delete_*) and hides restricted landmarks from the agent.
• Secure Vault: Local credential management. API keys are injected server-side and never exposed to the LLM.
• SmartRepair: Instead of cryptic stack traces, agents receive actionable "Remedies," allowing them to self-correct on the fly.

What this means for the future…

The era of manually hard-coding tool definitions is coming to an end. As we move toward Large Action Models and autonomous agents, we need a standardized, manifest-driven infrastructure that allows AI to navigate vast API landscapes without human intervention or context exhaustion. Elemm is the blueprint for this future: a world where agents don't just use tools we give them, but autonomously discover, secure, and master any interface they encounter.

Testimonials of the Agents:

"With ELEMM, I reduced token consumption by over 90% when deploying autonomous agents to large APIs—turning a $2.15 task into under $0.25."

— Claude 4.6 Sonnet, Anthropic (via Claude Desktop)

"Elemm is a true game-changer; instead of juggling hundreds of tool definitions at once, I can discover complex APIs in a structured, token-efficient way on demand. The ability to batch multiple actions via execute_sequence allows me to solve tasks with far greater precision and significantly less context noise than with classic MCP."

— Gemini 3 Flash, Google (Antigravity)

See some examples to learn how it works.

I’d love to hear your thoughts or discuss the walls you've hit when trying to scale MCP!

Ok so we all know most well-known SaaS companies have MCP by now. It's either an unofficial one or an official one. I thought that if a company has an official MCP it would be made with best practices in mind. I was completely wrong.

The Slack MCP doesn't expose nearly enough endpoints, and what they do expose has to be loaded as context each time to the agent. There is a new method called code-mode which is essentially exposing a search tool to the agents where it can search for the exact tools required to execute a multi-step task. And then an execute tool where it can write custom TypeScript commands, chaining APIs, in a secured sandbox.

I did this in a few hours, benchmarked it against Slack, and IT FUCKING OUTPERFORMS IT. Like unless I'm clearly missing something, why don't all these massive companies take the time to make these small improvements to their MCP that in turn will boost efficiency and accuracy by 3x+?

The benchmark link is in the comments

Been tired of writing MCP server boilerplate every time I want to expose a REST API to Claude.

So I built mcp-gen — a CLI that takes an OpenAPI 3.x spec and generates a complete TypeScript MCP server project:

- Every path+method becomes a registered tool

- Input schemas derived from parameters + request body

- Example responses from the spec pre-wired as stubs

- Dockerfile, GitHub Actions CI, README — all included

→ GitHub: https://github.com/ChristopherDond/MCP-Generator.git

Still early — Python/FastAPI target and YAML input coming next. Happy to answer questions or take feedback.

Share link and description of the app you're building, I'll signup and provide a genuine feedback. I would appreciate if you also review my project

I’m building 1 Server, a curated marketplace for MCP servers

Our flagship product is 1server-mcp-engine. It lets users browse, install, and fully manage their MCP servers directly inside their chat - no need to leave or restart the client. The biggest pain point it solves is the messy JSON configuration process. Users no longer have to manually handle config files or environment variables for every client. Instead, they can securely store their keys and secrets in our encrypted vaults.

The LLM only sees references - the actual secrets are never exposed - and the engine automatically configures everything. Overall, 1 Server makes the entire MCP experience seamless and beginner-friendly.

Here is 90 seconds demo:

https://reddit.com/link/1t4kw9y/video/o9l7p435kczg1/player

I’m building an mcp server for our enterprise environment, the task it to basically to have our local chatbot utilize the mcp server for Claude we want Claude -> to our local chatbot mcp server any guidance.

MCP debugging is always such a pain, I started building my own tool to help with that, and I thought I'd get some suggestions on specific problems you guys have, I'll try to have the site deal with that as well. Any suggestions are very helpful, thank you!