cti-mcp-server
Allows using local or custom Ollama models as the LLM backend for the threat intelligence agent, enabling offline or privacy-preserving analysis.
Enables AI agents using OpenAI-compatible APIs to call MCP tools for threat intelligence analysis, including IoC lookups, event backtracking, and IP enrichment.
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., "@cti-mcp-serverenrich IP 8.8.8.8"
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.
CTI MCP Server
CTI MCP Server is a lightweight MCP (Message/Tool Call Protocol) service framework that packages and exposes multiple tools for a Threat Intelligence Agent, enabling automated triage, evidence queries, and centralized integration.
Key Features
Lightweight: uses
fastmcpto wrap tools as remotely callable MCP methods.TI-focused: built-in IoC lookups, event backtracking, and basic IP enrichment.
Model-integrated: works with OpenAI and other LLMs so the model can call MCP tools during analysis to gather evidence.
Easy to debug and deploy locally with CLI (Typer) support.
Quick Start
Prepare Python (project requires Python >= 3.14) and create a virtual environment:
python -m venv .venv
source .venv/bin/activate
pip install -e .Start the MCP SSE service (defaults to 127.0.0.1:8000, path /mcp):
uv run cti-mcp-server start
# or
python -m cti_mcp_server.server startCustom host/port/path example:
cti-mcp-server start --host 0.0.0.0 --port 8000 --path /mcpEnable authentication (recommended for public internet exposure):
export CTI_MCP_AUTH_TOKEN="replace-with-a-long-random-token"
cti-mcp-server start --host 0.0.0.0 --port 8000 --path /mcpYou can also pass it directly:
cti-mcp-server start --auth-token "replace-with-a-long-random-token"When auth is enabled, clients must send header:
Authorization: Bearer <your-token>Validate locally with the Agent example (connects to local MCP service):
cti-agent 8.8.8.8Use custom model endpoint / key / model (OpenAI-compatible API):
cti-agent 8.8.8.8 \
--mcp-url http://127.0.0.1:8000/mcp \
--llm-base-url http://127.0.0.1:11434/v1 \
--llm-api-key ollama \
--model qwen3:latestcti-agent command arguments:
Positional argument:
ioc(required), e.g. IP/domain.--mcp-url: MCP service URL (defaulthttp://127.0.0.1:8000/mcp).--llm-base-url: OpenAI-compatible model API base URL.--llm-api-key: API key for the model endpoint.--model: model name (for exampleqwen3:latest).
Built-in MCP Tools (Examples)
ioc_type(ioc: str) -> str: Detects IoC type (IP / domain).local_summary(ioc: str) -> dict: Returns structured summary from the local intel store (verdict, tags, first_seen, etc.).local_events(ioc: str, limit: int=20) -> dict: Returns recent observed events (for evidence lists).ip_basic(ip: str) -> dict: Offline basic IP enrichment (private/public determination, geo/ASN placeholder info).
Development & Testing
Run tests:
pytestLint/format:
ruff .Dependency management and packaging via
pyproject.toml.
Contributing
Issues and PRs are welcome. Please describe the problem and purpose of changes in the PR, keep commits tidy, provide test coverage, and include a brief explanation.
License
See the LICENSE file in the repository (or otherwise negotiated if absent).
Thanks for using CTI MCP Server. For help, please open an issue in the repository.
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