u/Just_Vugg_PolyMCP

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.

The Model Context Protocol (MCP) is quickly becoming the standard way for AI agents (Claude, Cursor, ChatGPT, etc.) to connect to real tools and systems.

The problem? Making your own software available to these agents has usually been quite annoying — lots of custom wrappers and boilerplate.

PolyMCP makes it dead simple.

It’s a universal toolkit that lets you turn your existing Python or TypeScript software into proper MCP servers with almost zero effort.

Quick example (Python):

from polymcp import expose_tools_http

def create_support_ticket(user_email: str, description: str, priority: str):

"""Create a ticket in our internal support system"""

...

def get_order_status(order_id: str):

"""Check real-time order status from our database"""

...

def generate_sales_report(region: str, period: str):

"""Pull sales data and generate report"""

...

app = expose_tools_http(

tools=[create_support_ticket, get_order_status, generate_sales_report],

title="Acme Internal Tools",

description="Core business systems for AI agents"

)

The TypeScript version is equally straightforward.

Once you run it, any MCP-compatible agent can automatically discover and use your tools.

PolyMCP also includes:

UnifiedPolyAgent → orchestrate multiple MCP servers with any LLM (OpenAI, Anthropic, Ollama…)

PolyClaw → safe Docker-based autonomous agent for real workflows

Nice Inspector UI for testing tools and running agents

Skills system + CLI tools

In short: it lets you bring your actual software into the MCP ecosystem so AI agents can work with your real systems, not just demo functions.

Repo: https://github.com/poly-mcp/PolyMCP

What internal tools or software are you planning to make available to AI agents?

Would love to hear your thoughts or use cases!