TIB MCP Server
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., "@TIB MCP Serversearch for papers about artificial intelligence in 2024"
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.
TIB MCP
A Model Context Protocol (MCP) server for scholarly tools, built with FastMCP. MCP is an open standard that lets AI assistants interact with external tools and data sources through a unified protocol. This server exposes scholarly tools that any MCP-compatible client can discover and invoke.
Prerequisites
Python 3.14+ — the project includes a
.python-versionfile for tool compatibilityuv — used as the package manager, build system, and script runner
Related MCP server: MCP Server Demo
Quick Start
# Clone the repository
git clone https://gitlab.com/TIBHannover/orkg/tib-aissistant/tib-mcp.git
cd tib-mcp
# Create your local environment file
cp .env.example .env
# Install all dependencies (including dev tools)
uv sync
# Start the server
uv run python main.pyBy default the server starts in streamable-http mode, which is suitable for networked or remote access. For local MCP client integrations (e.g. Claude Desktop, VS Code with Copilot), you can switch to stdio mode.
Architecture
The runtime is split into small layers:
main.pyandserver/main.pyare entry points that start the FastMCP server.server/app.pybuilds the application instance (create_app) and registers tools.server/core/contains environment-based configuration (Settings, transport normalization).server/tools/exposes MCP tools and mounts them by namespace.server/services/contains business logic used by tools.server/utils/contains runtime helpers (for example transport-specific run kwargs).
Configuration
All configuration is done through environment variables. A .env file in the project root is automatically loaded on startup via python-dotenv. See .env.example for a documented template.
Variable | Description | Default | Valid Values |
| Server Name |
| any name |
| Transport protocol |
|
|
| Bind address (non-stdio transports) |
| Any valid host/IP |
| Port number (non-stdio transports) |
| Any valid port |
| Logging verbosity |
|
|
Running the Server
stdio Mode
Designed for direct integration with AI clients. The client launches the server as a subprocess and communicates over stdin/stdout:
uv run python main.pyFor example, to configure this server in an MCP client, point it at:
{
"mcpServers": {
"tib-mcp": {
"command": "uv",
"args": ["run", "python", "main.py"],
"cwd": "/path/to/tib-mcp"
}
}
}HTTP Mode
For networked or remote access, use the streamable-http transport:
# Via .env file
MCP_TRANSPORT=streamable-http
# Or inline
MCP_TRANSPORT=streamable-http uv run python main.pyThe server will be available at http://127.0.0.1:8000/mcp.
Try It with MCP Inspector
You can play around with the server using @modelcontextprotocol/inspector:
# Inspect via stdio (launches this server as a subprocess)
npx @modelcontextprotocol/inspector uv run python main.pyIf your server is already running in streamable-http mode:
npx @modelcontextprotocol/inspectorThen connect to http://127.0.0.1:8000/mcp from the Inspector UI.
Docker
The project includes a production-ready Dockerfile that defaults to streamable-http transport on 0.0.0.0:8000:
# Build the image
docker build -t tib-mcp .
# Run the container
docker run -p 8000:8000 tib-mcpOverride configuration with environment variables:
docker run -e MCP_LOG_LEVEL=DEBUG -p 8000:8000 tib-mcpRunning Tests
uv run pytestThe test suite is organized into four categories:
Category | Location | Description |
Unit |
| Direct function-level tests for each tool |
Config |
| Validates environment variable defaults and overrides |
Contract |
| Verifies tool metadata, descriptions, and JSON schemas |
Integration |
| End-to-end MCP client/server communication tests |
For verbose output:
uv run pytest -vProject Structure
tib-mcp/
├── main.py # Root entry point for running the MCP server
├── server/
│ ├── __init__.py
│ ├── app.py # App factory (create_app / get_application)
│ ├── main.py # Server module entry point + exported runtime constants
│ ├── core/
│ │ ├── __init__.py
│ │ └── config.py # Environment config + transport normalization
│ ├── services/
│ │ ├── __init__.py
│ │ └── sum.py # Sum service logic
│ ├── tools/
│ │ ├── __init__.py # Tool mounting (register_tools)
│ │ └── sum.py # Sum MCP sub-server and tool definition
│ └── utils/
│ ├── __init__.py
│ └── runtime.py # Runtime helpers (run kwargs per transport)
├── tests/
│ ├── __init__.py
│ ├── conftest.py # Shared fixtures (mcp_app, mcp_client)
│ ├── test_config.py # Configuration behavior tests
│ ├── test_contract.py # Tool metadata/schema contract tests
│ ├── test_integration.py # MCP client/server integration tests
│ └── tools/
│ ├── __init__.py
│ └── test_sum.py # Unit tests for sum behavior
├── pyproject.toml # Dependencies + tool configuration
├── uv.lock # Dependency lock file
├── Dockerfile # Container image definition
├── .dockerignore
├── .env.example # Environment variable template
├── .pre-commit-config.yaml # Pre-commit hooks (Ruff + checks)
├── AGENTS.md # Repository instructions for coding agents
├── CLAUDE.md
├── LICENSE
└── README.mdDevelopment
Pre-commit Hooks
The project uses Ruff for linting and formatting, configured via pre-commit:
uv run pre-commit installThis runs automatically on every commit, or manually with:
uv run pre-commit run --all-filesAdding a New Tool
Tools in this project follow the same pattern as sum: a service layer for logic, and a FastMCP sub-server for exposure.
Create the service in
server/services/my_tool.py:
class MyToolService:
def do_something(self, param: str) -> str:
return param.strip().upper()Export the service in
server/services/__init__.py:
from server.services.my_tool import MyToolService
__all__ = ["SumService", "MyToolService"]Create the MCP tool sub-server in
server/tools/my_tool.py:
from fastmcp import FastMCP
from server.services import MyToolService
server = FastMCP("my_tool")
my_tool_service = MyToolService()
@server.tool()
def do_something(param: str) -> str:
"""Short description of what this tool does.
Args:
param: Description of the parameter.
Returns:
Description of the return value.
"""
return my_tool_service.do_something(param)
Type hints define the input schema, and the docstring is exposed to MCP clients as tool description.
Mount the tool in
server/tools/__init__.py:
from fastmcp import FastMCP
from server.tools.my_tool import server as my_tool_server
from server.tools.sum import server as sum_server
def register_tools(app: FastMCP) -> None:
app.mount(sum_server, namespace="sum")
app.mount(my_tool_server, namespace="my_tool")With namespace mounting, this tool is discoverable as my_tool_do_something.
Add/update tests:
Unit test in
tests/tools/test_my_tool.py(direct function/service behavior).Contract test in
tests/test_contract.py(tool name, description, schema, tool count).Integration test in
tests/test_integration.py(call the tool via MCP client).
Then run:
uv run pytest
uv run pre-commit run --all-filesLicense
This project is licensed under the MIT License. © 2026 ORKG/TIB Team
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