What is the default model and does it require cloud access?
The default model is `qwen3:14b`. Any function‑calling model that Ollama exposes will work. No cloud keys are required; everything runs locally.
MCP-Ollama Client
@Nagharjun17
Basic information
About MCP-Ollama Client
Lightweight MCP client that uses a local Ollama LLM to query multiple MCP servers defined in config.json
MCP-Ollama Client config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MCP-Ollama-Client": {
"command": "uv",
"args": [
"venv"
]
}
}
}MCP-Ollama Client tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is MCP-Ollama Client?
MCP-Ollama Client is a command‑line chat client that runs entirely offline with a local LLM via Ollama and connects to any number of Model Context Protocol (MCP) servers declared in a single config.json. It is designed for developers who want to use local function‑calling models together with MCP tools.
How to use MCP-Ollama Client?
Clone the repository, set up a Python ≥ 3.12 virtual environment with uv, install dependencies, pull a local model with ollama pull qwen3:14b, edit the model name and server settings in config.json, then run uv run client.py. At startup the client launches every MCP server, aggregates their tool schemas with server‑name prefixes (e.g. postgres.*, filesystem.*), and presents the merged list to the LLM.
Key features of MCP-Ollama Client
- Runs entirely offline with a local LLM via Ollama
- Connects to multiple MCP servers side‑by‑side
- Collision‑free tool names via server‑name prefixing
- Everything configured in one
config.jsonfile - No cloud API keys required
Use cases of MCP-Ollama Client
- Query a local database with natural language using MCP tools
- Read or write files on a remote share through a filesystem MCP server
- Combine data from multiple MCP sources in a single conversation
- Test and debug MCP server integrations without a cloud LLM
Frequently asked questions
What are the runtime dependencies?
Python ≥ 3.12, Ollama ≥ 0.8.0, and any MCP servers that support **stdio** transport.
How does tool naming work when multiple servers are used?
Tools are exposed as `<server>.<tool>` (for example `postgres.list_schemas`, `filesystem.read_file`) so names never clash, even when different servers provide tools with the same name.
Where is data stored and can I add or remove servers without changing code?
Data stays entirely local. Servers are defined in `config.json` under the `mcpServers` key; you can add or remove servers by editing that file without touching `client.py`.
What transport does the client use to communicate with MCP servers?
Each MCP server is started as its own **stdio** subprocess. No network transport is used between the client and the servers.
More AI & Agents MCP servers
Kourier
Kourier AI Inc.Discover flat-rate DeepSeek plans, model limits, API-key instructions, and coding-client setup
DatePhotos.AI
Jacob YangFree MCP server for dating profiles. Look up the official photo rules and AI-photo policies of Tinder, Hinge and Bumble — photo limits, verification, what gets you banned — each quoted from the app's own guidelines wi
scvd.store
seancrecordscvd.store is an evidence observatory for agentic commerce. Independent signed observation of what other people's endpoints, artifacts and payments actually did among other things. USDC on Base, Arbitrum One, World Chain
2
Rokha
Rokha AI LLCSearch a registry of agent skills and MCP servers, run them for real (sandbox or remote MCP, no install, a trace on every run), and hire proven agents for USDC.
q-ring
I4cTimeQuantum-inspired keyring for AI coding agents. Secure secrets with superposition, entanglement, tunneling, and teleportation — CLI + 44 MCP tools.
3
Comments