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Documentation
MCP server for ClearML: browse projects, search and compare tasks, list datasets and models — all via Model Context Protocol.
- PyPI: https://pypi.org/project/mcp_clearml/
- License: MIT
- Docs: https://mcp-clearml.readthedocs.io
What you get
This server exposes a curated set of ClearML operations as MCP tools:
- Projects
list_of_all_projects: list all projects (id, name)find_project_by_pattern: find by name substring (case-insensitive)get_project_stats: aggregated counters for a project (statuses, users, tags, days)
- Tasks (bulk/filters)
get_tasks_core_info: core fields by task idsget_tasks_full_info: core + parameters/metrics/artifacts/models by task idsfind_tasks_core_info_by_pattern: IDs by name/status/tagsfind_tasks_full_info_by_pattern: full profiles by name/status/tagsget_tasks_core_info_by_project: stats + core list for a projectget_tasks_full_info_by_project: stats + full profiles for a project
- Models
find_models_by_pattern: search models by name fragment (includes url/uri when present)find_models_info: info for specific model ids
- Datasets
find_datasets_by_project: list datasets in a project (optional recursive)find_datasets_by_pattern: list datasets by partial nameget_datasets_full_info: per-dataset sizes, uploader and parsed metadata (csv/csv.gz/json)
- Compare
compare_tasks: cross-task summary + aligned metrics + parameters diff
See also in-code guides to help LLMs choose the right tool:
- Categories:
mcp_clearml.docs.CATEGORY_GUIDE - Per-tool hints:
mcp_clearml.docs.TOOL_GUIDE
Requirements
- Python >= 3.12
- ClearML account with valid credentials in
~/.clearml/clearml.conf - Recommended
uvto run viauvxwithout installing globally
Quick Start
Prerequisites
Ensure your ~/.clearml/clearml.conf contains your credentials:
[api]
api_server = https://api.clear.ml
web_server = https://app.clear.ml
files_server = https://files.clear.ml
credentials {
"access_key": "your-access-key",
"secret_key": "your-secret-key"
}
You can obtain keys in ClearML Settings.
Install / Run
- Install from PyPI:
pip install mcp_clearml
- Run without installation (via uvx):
uvx mcp-clearml
- Local dev:
uv sync
uv run mcp-clearml
Run MCP server (stdio)
The mcp-clearml command validates ClearML connectivity and starts an MCP stdio server.
Integrations (MCP clients)
Claude Desktop
Config file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"clearml": {
"command": "uvx",
"args": ["mcp-clearml"]
}
}
}
Alternative (if installed via pip):
{
"mcpServers": {
"clearml": {
"command": "python",
"args": ["-m", "mcp_clearml.mcp"]
}
}
}
Cursor
Settings → MCP → Add Server:
{
"mcp.servers": {
"clearml": {
"command": "uvx",
"args": ["mcp-clearml"]
}
}
}
You can also add a rule in .cursorrules to remind using the clearml MCP server for experiment analysis.
Continue
~/.continue/config.json:
{
"mcpServers": {
"clearml": {
"command": "uvx",
"args": ["mcp-clearml"]
}
}
}
Cody
{
"cody.experimental.mcp": {
"servers": {
"clearml": {
"command": "uvx",
"args": ["mcp-clearml"]
}
}
}
}
Any MCP‑compatible assistant
{
"mcpServers": {
"clearml": {
"command": "uvx",
"args": ["mcp-clearml"]
}
}
}
Verified with: Zed, OpenHands, Roo‑Cline, and others.
Use in Claude Code (or any MCP client)
Add the stdio server with the same command. Then call tools by name (e.g., "find tasks by pattern", "compare_tasks"). Generic MCP UIs will connect over stdio automatically.
Examples
- "List projects" →
list_of_all_projects - "Stats for project X" →
get_project_stats { project_name: "X" } - "Find tasks by name fragment and get full details" →
find_tasks_core_info_by_pattern { task_name_pattern: "resnet" }get_tasks_full_info { task_ids: ["..."] }
- "Compare two tasks" →
compare_tasks { task_ids: ["task_id_1", "task_id_2"] } - "Find datasets by project" →
find_datasets_by_project { project_name: "X", recursive_project_search: true }
Development
Setup & test locally:
uv sync --extra test
uv run pytest -q
Coverage gate is configured at 65% (see [tool.coverage.*] in pyproject.toml).
Lint/type check:
uv run ruff check --output-format=github src/ tests/
uv run ty check || true
Release (GitHub Actions)
- Tests run on all branches/PRs (
.github/workflows/tests.yml). - Tag‑based release (
.github/workflows/release.yml):- push tag
vX.Y.Z→ runs tests (verify) - build wheels/sdist, create GitHub Release with artifacts
- optional PyPI publish if
PYPI_API_TOKENis set
- push tag
See docs/releasing.md for the exact steps.
Credits
Acknowledgment: thanks to prassanna-ravishankar for the ClearML MCP project that inspired this work.
