custom-mcp-server
Exposes custom Python functions as tools and integrates with Ollama for tool calling.
README
Ollama MCP Demo
This demo shows how to create a custom MCP server to expose custom Python functions as tools. It also demonstrates how a custom MCP client class can be used to integrate the MCP server with Ollama. Both, the MCP server and Ollama are independent and can be run on different machines.
Add Tools to your MCP Server
To add new tools to the MCP server, simply create Python functions in mcp_server/tools. Once created, add them to the TOOLS tuple in mcp_server/__main__.py which is used to register them to the MCP server.
# mcp_server/__main__.py
from mcp_server.tools import echo
...
SERVER = FastMCP(name="custom-mcp-server", **SERVER_CONFIG)
TOOLS = (echo,) # add functions here
...
Running the MCP Server
-
(Optional) Configure the environments in the
docker-compose.ymlfile (ports, ollama configs, ...). -
Start the services. This spins up an Ollama instance and the MCP server.
docker compose up -d -
(Optional) To download ollama models once the containers are running, use
docker compose exec ollama ollama pull <your model>
The Ollama server can then be accessed at http://localhost:11434 and the MCP server at http://localhost:7777/mcp (replace ports with your configuration).
MCP Client Usage
Install the dependencies used for the MCP client
uv sync
You can then use the mcp_client.client.MCPClient class to communicate with the MCP server like this:
from mcp_client.client import MCPClient
mcp_client = MCPClient(host="localhost", port=7777)
# list available tools
tools = await mcp_client.list_tools()
...
# call a tool
result = await mcp_client.call_tool(tool_name="some_tool", arguments={"some_arg": "value"})
...
Integrating it with Ollama can be done like so:
from mcp_client.client import MCPClient
from ollama import Client as OllamaClient
mcp_client = MCPClient(host="localhost", port=7777)
ollama_client = OllamaClient("http://localhost:11434")
# invoke llm
response = ollama_client.chat(
model="qwen3:4b",
messages=[{"role": "user", "content": "Echo this message 'Hi, Alice!'"}],
tools=await mcp_client.list_tools(),
)
print(response.message.content)
# handle tool calls
if tool_calls := response.message.tool_calls:
for tool_call in tool_calls:
tool_name = tool_call.function.name
arguments = tool_call.function.arguments
print("Calling", tool_name, "with arguments", arguments)
tool_result = await mcp_client.call_tool(tool_name, arguments)
print("Result: ", tool_result)
Helpful Links
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.