custom-mcp-server
Provides tools that can be used with Ollama's tool calling API, enabling AI agents to invoke custom functions through the Ollama chat interface.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@custom-mcp-serverEcho this message 'Hello, world!'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
...Related MCP server: Ollama MCP Server
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 syncYou 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
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
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