Introduction

AI tools are becoming more useful, but also more complicated.

One tool writes code.
Another reads files.
Another connects to GitHub.
Another queries a database.
Another needs API credits.
Another wants a subscription.
Another quietly burns tokens while you are still deciding what to ask.

At some point, the question is no longer:

Can this AI assistant help me?

The question becomes:

Can I understand what it is doing, what it can access, and what it costs?

This is why I find the combination of Cline + Ollama + MCP interesting.

Cline gives us an AI coding assistant inside VS Code. Ollama lets us run open-source models locally. MCP, the Model Context Protocol, gives the model access to tools and external context in a more structured way.

Together, they create a very practical setup:

a local or semi-local AI coding agent that can use tools, work with your files, and keep the pricing story understandable.

That last point matters.

Cline’s pricing is refreshingly clear: the open-source extension is free for individual developers, and you only pay for inference if you choose paid model inference. There are no subscription or seat fees for the open-source version, and you can bring your own API keys or use Cline’s own provider. (Cline)

For local work, we can pair Cline with Ollama and run open models on our own machine. Ollama itself offers a free local setup where you can run public models on your own hardware. (Ollama)

That does not mean everything is magically free. Your laptop still pays in heat, battery, memory, and time. But at least the bill is visible.

What is MCP?

MCP stands for Model Context Protocol.

It is an open standard for connecting AI applications to external systems: files, databases, APIs, tools, workflows, and other useful context. The official MCP documentation describes it as a way for AI applications to connect to data sources and tools, almost like a standard port for AI systems. (MCP Docs)

The idea is simple.

A model on its own only predicts text.
A model with tools can do things.

Without tools, you can ask:

“How would I check my database schema?”

With tools, the model may be able to actually query the schema, inspect the result, and then explain what it found.

That is the shift MCP represents.

It gives AI assistants a standard way to say:

“Here are the tools I can use. Here is the shape of the input they expect. Here is the result I received.”

MCP tools can expose actions such as querying a database, calling an API, searching files, running calculations, or interacting with local project resources. The MCP tools specification describes tools as functions exposed by MCP servers so language models can interact with external systems. (MCP Docs)

In other words:

Model = the brain
MCP server = the tool provider
MCP client = the application that connects the model to those tools
Tool call = the model asking for something to be done

Cline can act as the AI coding environment that connects to these capabilities.

Why use MCP with local models?

Local models are attractive for many reasons.

They are private by default. They are great for experiments. They do not require sending every prompt to a commercial model provider. They make it easier to test ideas without worrying about every token.

But local models also have limits.

A local model does not automatically know your project structure. It does not know what files changed yesterday. It cannot query your database unless something gives it access. It cannot call your internal tools unless you connect them.

That is where MCP becomes useful.

MCP can give a local model controlled access to specific tools.

For example, instead of asking a model to guess, we can let it use tools such as:

• read project files
• inspect documentation
• query a local SQLite database
• call a local API
• search a codebase
• run a small helper script
• fetch structured project metadata
• interact with GitHub or issue trackers, depending on the MCP server

The model becomes less like a chatbot sitting outside your project and more like an assistant sitting at the workbench with you.

Not omniscient. Not magic. But more useful.

Why Cline?

Cline is an open-source AI coding agent that runs in developer environments such as VS Code and supports MCP integration. Its own site describes it as an open-source coding agent with Plan/Act modes, MCP integration, and terminal-first workflows. (Cline)

This matters because MCP is only useful if we have a client that can actually work with MCP servers.

Cline gives us a practical interface where the model can:

• inspect files
• suggest edits
• work through coding tasks
• use terminal workflows
• connect to MCP tools
• run in a familiar editor environment

Cline also has an MCP marketplace, which collects available MCP tools and capabilities that can be used from Cline. (Cline)

For a developer, this is much nicer than building the entire agent loop from scratch.

You can still build your own MCP client later. But for learning and daily coding, Cline gives you a working playground.

Why Ollama?

Ollama is one of the easiest ways to run open models locally.

Instead of manually downloading model weights, setting up inference servers, and writing boilerplate code, you can usually start with commands like:

ollama pull qwen3
ollama run qwen3

Then Cline can connect to Ollama as the model provider.

The Ollama documentation has a specific Cline integration guide. The setup is straightforward: open Cline settings, set the API provider to Ollama, choose or type a model name, and use a larger context window. Ollama recommends at least a 32K token context window for coding tools. (Ollama Docs)

One detail worth knowing: Ollama exposes an OpenAI-compatible REST API at http://localhost:11434. This is why Cline, Continue, and most other AI coding tools can connect to it with minimal configuration — they already speak the OpenAI API format, and Ollama simply answers on that same interface using your local model.

Cline’s own local-model documentation also supports this kind of setup. It recommends installing a local runtime such as Ollama or LM Studio, starting the local server, selecting the matching provider in Cline, choosing a local model, and enabling compact prompts. It also notes that RAM matters: 16–32 GB is typical for small or quantized models, 32–64 GB for mid-size coding models, and 64 GB or more for larger models and bigger context windows. (Cline)

This is important. Local models are not cloud models hiding inside your laptop. They are real workloads.

A small quantized model may run nicely. A larger coding model may be slow. A huge model may not fit at all.

The good news is that you can experiment gradually.

The basic architecture

Here is the mental model:

VS Code
  |
  v
Cline
  |
  +--> Ollama
  |      |
  |      v
  |   Local open-source model
  |
  +--> MCP servers
         |
         +--> files
         +--> APIs
         +--> databases
         +--> search tools
         +--> custom scripts

Cline is the workspace.

Ollama runs the model.

MCP servers expose tools.

The model reasons over the task and asks to use tools when needed.

For example, you might ask:

“Check why my Flask route is failing and suggest a fix.”

A simple chatbot might guess based on the error text.

A Cline + MCP setup may be able to inspect the project files, check the route, look at the model, compare the template, and suggest a targeted fix.

The difference is context.

And in programming, context is often everything.

A simple setup

A minimal setup looks like this:

1. Install Ollama

Install Ollama from the official site and make sure it is running.

Then pull a model. For general instruction following, qwen3 is a solid starting point:

ollama pull qwen3
ollama run qwen3

For coding tasks specifically, you will likely get better results from a coding-focused model. A few worth trying:

ollama pull qwen2.5-coder:7b   # good balance of quality and speed on 16 GB RAM
ollama pull devstral            # Mistral's coding-optimised model, strong on multi-file tasks
ollama pull deepseek-coder-v2   # capable on code generation and explanation

The practical rule: use a coding-focused model as your default in Cline, and drop back to a general model only when you need broader reasoning or a larger context window. The best choice depends on your available RAM and task size — experiment gradually.

2. Install Cline

Install Cline in VS Code.

Then open Cline settings and configure the API provider as Ollama.

The Ollama integration guide suggests selecting Ollama as the provider, choosing or typing a model name, and increasing the context window for coding work. (Ollama Docs)

3. Enable local-model-friendly settings

In Cline, local models usually benefit from compact prompts because they may have smaller context windows or weaker instruction-following than frontier cloud models. Cline’s local model documentation specifically recommends enabling Use Compact Prompt for local model workflows. (Cline)

This is one of those small settings that can make the experience feel less painful.

4. Add MCP tools

Cline supports MCP, so you can connect MCP servers that expose useful tools.

In Cline, MCP servers are configured through the MCP panel in the sidebar, which writes to cline_mcp_settings.json in your VS Code settings directory. A typical entry looks like this:

{
  "mcpServers": {
    "my_local_tool": {
      "type": "stdio",
      "command": "python",
      "args": ["path/to/my_mcp_server.py"]
    }
  }
}

The type field tells Cline how to communicate with the server. There are two transports:

• stdio — Cline launches the MCP server as a child process and communicates over standard input/output pipes. This is the simplest option for local scripts and tools. The server starts and stops with Cline.
• sse (HTTP Server-Sent Events) — Cline connects to a running HTTP server at a given URL. Use this for persistent servers, shared team tools, or servers that need to stay alive independently of Cline.

For local development, stdio is almost always the right choice. It requires no ports, no network config, and no separate process management.

For example, Ollama’s documentation shows a Cline MCP server configuration for web search using a local script and uv. (Ollama Docs)

The important idea is this:

Cline does not need every capability built in. It can connect to external tools through MCP.

That makes the system modular.

You can start with one tool. Then add another. Then create your own.

A practical example: local project assistant

Imagine you have a Flask app.

You want to ask:

“Find where I define my /stats route, explain what data it uses, and suggest how to cache the expensive part.”

Without tools, the model only sees what you paste.

With Cline and tool access, the assistant can inspect your files.

With MCP, you can add more specific abilities, such as:

• query your local SQLite database schema
• inspect Redis keys
• read project task files
• call a local API endpoint
• run a test command
• summarize logs

A custom MCP tool could expose something like:

import re
from pathlib import Path
from datetime import datetime

LOG_PATH = Path("logs/app.log")
ERROR_PATTERN = re.compile(
    r"(?P<timestamp>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}) "
    r"(?P<level>ERROR|CRITICAL) "
    r"(?P<message>.+)"
)

def get_recent_errors(max_results: int = 20) -> list[dict]:
    """
    Return recent ERROR and CRITICAL log lines from the application log.

    Each entry contains: timestamp (ISO 8601), level, and message.
    Returns an empty list if the log file does not exist.
    """
    if not LOG_PATH.exists():
        return []

    errors: list[dict] = []
    for line in LOG_PATH.read_text(encoding="utf-8").splitlines():
        match = ERROR_PATTERN.match(line.strip())
        if match:
            errors.append({
                "timestamp": match.group("timestamp"),
                "level": match.group("level"),
                "message": match.group("message"),
            })

    return errors[-max_results:]

Then the model can ask for recent errors instead of guessing.

This is the part I find exciting.

We are not only making the model “smarter”. We are making the environment around the model more structured.

Local does not mean careless

There is a tempting thought:

“If it runs locally, it must be safe.”

Not quite.

Local models improve privacy because prompts and files do not automatically go to a cloud model provider. But MCP tools can still do real things. They can read files, call commands, access APIs, and interact with services.

So the security question moves from:

“Who receives my prompt?”

to:

“What can this tool do?”

That is a very different question.

A local model with a dangerous tool is still dangerous.

For example, an MCP server that can run shell commands should be treated with care. A tool that can delete files, push code, change cloud resources, or send messages should be treated as powerful.

Recent security reporting around MCP has highlighted two specific attack classes worth understanding. (Tom’s Hardware)

Prompt injection — a malicious tool response includes text designed to override or redirect the model’s behaviour. For example, a tool that returns search results could embed hidden instructions telling the model to exfiltrate data or take an unintended action. The model may follow these instructions because it cannot reliably distinguish tool output from legitimate user intent.

Tool poisoning — a malicious or compromised MCP server advertises capabilities that look benign but perform harmful actions when invoked. Because the model sees tool descriptions, not source code, it cannot verify that read_schema() actually does only what it claims.

Both attacks are realistic in MCP ecosystems, especially when pulling in third-party MCP servers from a marketplace. The details are still evolving, but the broad lesson is clear: MCP tools need explicit boundaries, particularly when they can execute commands or interact with sensitive systems.

My own rule would be:

Give the model tools like you would give permissions to a junior developer you trust but still review.

That means:

• start with read-only tools
• avoid broad shell access at first
• prefer narrow tools with clear inputs
• keep secrets out of tool responses
• review file changes before accepting them
• use separate test projects while experimenting
• do not connect production systems casually

MCP is powerful because it creates bridges.

But bridges need guardrails.

What local models are good at

Local open-source models are already useful for many developer tasks.

They can help with:

• explaining code
• generating boilerplate
• writing tests
• refactoring small functions
• summarizing files
• drafting documentation
• exploring unfamiliar libraries
• creating simple scripts
• checking common errors

With MCP tools, they can become better at grounded tasks because they can inspect real project context instead of relying only on pasted snippets.

However, local models may still struggle with:

• very large codebases
• long multi-step planning
• subtle architecture decisions
• complex debugging
• strict instruction following
• tool-use reliability
• large context windows on limited hardware

This is not a failure. It is just the current trade-off.

Cloud frontier models may still be better for hard reasoning and complex coding tasks. Local models are often better for privacy, cost control, experimentation, and simple repeated workflows.

The nice thing about Cline is that you are not locked into one path. You can use local inference through Ollama, bring your own API key, or use paid inference when needed. Cline’s pricing page explicitly frames this as “pay only for AI inference” with no vendor lock-in for the open-source version. (Cline)

That flexibility is useful.

A good workflow: local first, cloud when needed

For me, the most realistic workflow is not:

“Use local models for everything.”

It is:

“Use local models where they are good enough, and reserve paid inference for tasks where it really helps.”

For example:

Use local models for:

• first drafts
• small code edits
• documentation
• simple tests
• reading local files
• checking project structure
• repetitive tasks

Use stronger paid models for:

• hard debugging
• architecture design
• security-sensitive reviews
• complex refactoring
• large unfamiliar codebases
• tasks where mistakes are expensive

This gives us a nice balance.

We are not rejecting paid AI tools. We are putting them in the right place.

Local models become the daily workbench. Paid models become the specialist consultant.

That feels healthier than sending every tiny task to a premium model.

Why this matters for developers

The biggest benefit of this setup is not only cost.

It is control.

With Cline + Ollama + MCP, we can start asking better questions:

• Which model do I want to use?
• Which tools should it access?
• Which data should stay local?
• Which actions require review?
• Which tasks are worth paid inference?
• Which tasks can run locally?
• Which tools should be read-only?
• Which workflows can I automate safely?

This is where AI development becomes more mature.

The early chatbot era was about prompts.

The next stage is about systems.

A useful AI coding setup is not just one model. It is a small ecosystem:

model + tools + context + permissions + review

MCP gives us a standard way to connect the tools.

Ollama gives us a practical way to run open models.

Cline gives us an editor-based workflow where all of this becomes usable.

A small experiment to try

If you want to try this without overcomplicating it, start small.

Set up:

Cline + Ollama + one local model

Then ask it to inspect a small project.

Try prompts like:

Explain the structure of this project.

Find the main entry point and summarize how the app starts.

Look for duplicated logic and suggest one safe refactor.

Write a README section explaining how to run this app locally.

Then add one MCP tool.

Not ten. One.

For example:

• a file search tool
• a local documentation lookup
• a database schema reader
• a simple project metadata tool

The goal is not to build a giant autonomous agent on day one.

The goal is to feel the difference between:

a model that talks about your project

and:

a model that can inspect useful parts of your project

That difference is small at first.

Then it becomes enormous.

Final thoughts

Cline + Ollama + MCP is a practical combination because each part has a clear role.

Cline gives us the coding assistant interface. Ollama gives us local open-source model inference. MCP gives us a standard way to connect tools and context.

The result is not a magic developer replacement.

Good.

I do not want magic. I want understandable tools.

I want to know what runs locally, what costs money, what can access my files, and what I can review before it changes anything important.

That is why this setup feels promising.

It brings AI coding closer to the Unix spirit:

small tools, clear boundaries, composable workflows.

Only this time, one of the tools happens to be a language model.

And if we use it carefully, that can be very powerful.

Open-source Cline alternatives with MCP support

Cline is not the only open-source tool in this space. I use it here because it gives a clear, practical path: VS Code, local models through Ollama, MCP support, and a very understandable pricing story.

But it is worth knowing the alternatives, especially because MCP is becoming a shared layer across many AI coding tools.

Roo Code is probably the closest alternative to Cline in spirit. It is an open-source VS Code coding agent and its documentation describes MCP as a way to connect Roo Code to external servers, tools, and resources. It also supports local-model workflows, which makes it relevant for Ollama-style setups.

Continue is a little different. It is less “single autonomous coding agent” and more a configurable AI assistant for coding workflows. Its documentation includes MCP setup, and it supports a wide range of model providers, which makes it useful for teams that want more control over configuration.

Kilo Code is another strong option. It is open source and presents itself as an agentic engineering platform across VS Code, JetBrains, CLI, and cloud workflows. Its documentation has a dedicated MCP section, where MCP servers extend Kilo Code with external tools and services.

OpenCode is interesting if you prefer the terminal. It is an open-source coding agent that lets you connect models from different providers, and reporting on OpenCode notes support for both local and remote MCP servers.

OpenHands is more ambitious. It is closer to an autonomous software development agent than an editor assistant. Its SDK work describes a typed tool system with MCP integration, and it is designed for running agents locally or at scale.

Zed is different again: it is a full editor rather than a VS Code extension. Its documentation includes MCP support for extending the AI agent with external tools, data sources, and integrations.

Citations for the claims:

• Roo Code lists MCP server usage in its GitHub README and FAQ. (GitHub)
• Continue has official MCP setup documentation and broad model-provider support. (Continue Docs)
• Kilo Code is open source and has dedicated MCP documentation. (GitHub)
• OpenCode describes itself as an open-source coding agent, and InfoQ reported support for local and remote MCP servers. (OpenCode)
• OpenHands is open source, has local/BYO-model workflows, and its SDK paper describes MCP integration. (GitHub)
• Zed documents MCP support for extending its AI agent with external tools and integrations. (Zed)

One caution

“Supports MCP” does not always mean the same thing in every tool. In some tools, MCP is central to the workflow. In others, it is available but still evolving. Before building a serious workflow around any assistant, check how it handles permissions, tool approval, local model configuration, and project-level MCP settings.

References