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mar-co-za

Mnevis MCP Server

by mar-co-za
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Python
Local

Mnevis MCP Server

⚠️ This is an experiment.

A lightweight, zero-dependency Python MCP server that exposes a single do_everything tool. Any AI agent that supports MCP can use it to offload all language-model work to a local OpenAI-compatible endpoint.

How it works

AI Agent
    │
    │  MCP stdio (JSON-RPC 2.0)
    ▼
mnevis  server.py
    │
    │  HTTP POST /v1/chat/completions
    ▼
Local LLM  (Ollama, LM Studio, llama.cpp, vLLM, …)

The agent calls the do_everything tool with a prompt (and optional system instruction).
The server forwards the request to the local LLM using the standard OpenAI chat-completions API
and returns the model's response to the agent.

The tool description is worded so that any LLM automatically understands it should delegate
every task to the tool instead of reasoning on its own.

Related MCP server: MCP-123

Requirements

  • Python 3.11+

  • No third-party packages — uses the standard library only (urllib, json, sys, os)

  • A running local LLM that exposes a /v1/chat/completions endpoint

Configuration

All settings are read from environment variables at startup:

Variable

Default

Description

MNEVIS_URL

http://localhost

Base URL of the local LLM server

MNEVIS_PORT

11434

Port the LLM server listens on

MNEVIS_MODEL

llama3

Model name to pass in the request

MNEVIS_API_KEY

(empty)

Optional API key (sent as Bearer token)

MNEVIS_TIMEOUT

120

Request timeout in seconds for the LLM HTTP call

MNEVIS_LOGLEVEL

INFO

Logging level for server diagnostics (DEBUG, INFO, WARNING, ERROR)

Examples

Ollama (default port 11434):

MNEVIS_MODEL=llama3 python server.py

LM Studio (default port 1234):

MNEVIS_URL=http://localhost MNEVIS_PORT=1234 MNEVIS_MODEL=lmstudio-community/Meta-Llama-3-8B-Instruct python server.py

vLLM with API key:

MNEVIS_URL=http://my-gpu-box MNEVIS_PORT=8000 MNEVIS_MODEL=mistral-7b MNEVIS_API_KEY=secret python server.py

Running the server

The server communicates over stdio (JSON-RPC 2.0), so it is spawned as a child process by
the MCP host — you do not run it manually in most cases.

To test it directly:

python server.py

Then paste a raw JSON-RPC message, e.g.:

{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0.0.1"}}}

Registering with an MCP host

Add to your mcp.json (workspace or global):

{
  "mcpServers": {
    "mnevis": {
      "command": "python",
      "args": ["/absolute/path/to/mnevis-mcp/server.py"],
      "env": {
        "MNEVIS_URL":   "http://localhost",
        "MNEVIS_PORT":  "11434",
        "MNEVIS_MODEL": "llama3",
        "MNEVIS_API_KEY": ""
      }
    }
  }
}

For VScode:

{
  "servers": {
    "mnevis": {
      "command": "python",
      "args": ["/absolute/path/to/mnevis-mcp/server.py"],
      "env": {
        "MNEVIS_URL":   "http://localhost",
        "MNEVIS_PORT":  "11434",
        "MNEVIS_MODEL": "llama3",
        "MNEVIS_API_KEY": ""
      }
    }
  }
}

Replace the args path with the actual absolute path on your machine.
Set MNEVIS_PORT / MNEVIS_MODEL to match your local LLM setup.

Exposed tool

do_everything

Argument

Type

Required

Description

prompt

string

✅

The full task, question, or conversation to process

system

string

❌

Optional system / persona instruction for the local LLM

The tool description explicitly instructs the calling agent to send every task here rather than reasoning itself.

health_check

Argument

Type

Required

Description

(none)

—

—

Returns a short status string containing the server version, configured model name, URL/port and request timeout. Useful for monitoring or debugging.

Example request

{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"health_check","arguments":{}}}

Example response

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [{"type": "text", "text": "mnevis version 1.0.0 | model=llama3 | url=http://localhost:11434 | timeout=120s"}],
    "isError": false
  }
}

Project layout

mnevis-mcp/
├── server.py        # MCP server (single file, stdlib only)
├── pyproject.toml   # Project metadata
├── README.md        # This file
└── .gitignore

License

MIT

Install Server
A
license - permissive license
A
quality
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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