scigantic-mcp
A Model Context Protocol server that exposes theScigantic catalog of public scientific data archives to any MCP client.
It is built to drop into Kiro for Life Sciencesalongside its domain database servers. Where each Kiro server wraps one domain'sAPIs (genomics, proteomics, structural, …), Scigantic is the cross-domainlaunchpad: 5,000+ curated public archives spanning every domain, each with anLLM-ready schema card (file format, columns, sample rows/headers, inlinedREADMEs/data dictionaries, and a copy-paste starter cell) so an agent canunderstand a dataset's structure before downloading anything.
It is discovery-only and zero-config — every tool calls public, read-onlyScigantic endpoints, so there is no API key to set up.
Requires Python 3.10+ and MCP SDK 2.x.
Prefer the hosted server if your client speaks HTTP. Scigantic also runs aremote MCP server at
https://api.scigantic.com/mcp(no auth, nothing toinstall, and it carries two extra tools):claude mcp add --transport http scigantic https://api.scigantic.com/mcp.This package exists for clients that launch stdio servers, such as Kiro.
Tools
| Tool | What it does |
|---|---|
search_archives(query, category?, limit?) |
Natural-language search across the whole catalog. |
get_archive(id) |
Full metadata for one archive. |
get_schema_card(id) |
The compact schema card — the fastest way to learn a dataset's structure. |
get_data_access(id, language?) |
How to load the dataset in your own environment: storage location + copy-paste code snippets. |
list_archive_files(id, limit?) |
A sample of the files/objects in the archive. |
Prompts (guided workflows)
These surface as slash commands in Claude Code (/mcp__scigantic__<name>):
| Prompt | What it does |
|---|---|
explore_dataset(topic) |
Search → inspect schema cards → recommend the best dataset → offer load code. |
start_analysis(archive_id, goal?) |
Pull schema card + data-access snippet for an archive and outline an analysis plan. |
Install & register in Kiro
Add an entry under mcpServers in ~/.kiro/settings/mcp.json.
Option A — uvx (zero-install, recommended):
{
"mcpServers": {
"scigantic": {
"command": "uvx",
"args": ["scigantic-mcp"]
}
}
}
Option B — install into a venv (matches the Kiro servers' own mcp.json form):
python3 -m venv .venv && . .venv/bin/activate
pip install scigantic-mcp # or: pip install /path/to/scigantic-mcp
{
"mcpServers": {
"scigantic": {
"command": "/path/to/.venv/bin/scigantic-mcp",
"env": {
"SCIGANTIC_API_URL": "https://api.scigantic.com"
}
}
}
}
Works the same in Claude Desktop / Claude Code (claude mcp add scigantic -- uvx scigantic-mcp)or any MCP client that launches stdio servers.
Configuration
| Env var | Default | Purpose |
|---|---|---|
SCIGANTIC_API_URL |
https://api.scigantic.com |
API base (set to https://staging-api.scigantic.com for staging). |
SCIGANTIC_API_ORIGIN |
https://scigantic.com |
Origin header → selects the public (default) catalog tenant. |
Stability
This package is a thin client over the public Scigantic REST API. That API isnot versioned and may change without notice — if a response shape moves, apinned older release of this package can break. Pin a version you have tested,and open an issue if a tool starts returning something unexpected.
The MCP tool names and their arguments are treated as the stable surface, andwill not change without a minor version bump.
Develop & test
The tool/client layer has no mcp dependency, so those tests run on any Pythonwith httpx and need no network (mocked transport):
python3 tests/test_tools.py # or: pytest
tests/test_server_import.py covers the wiring layer — that the server moduleimports, and that the registered tools and prompts are the expected set. It needsthe mcp SDK installed (Python ≥ 3.10) and is skipped otherwise:
pip install -e '.[test]' && pytest
Keep it that way: the tool tests skip server.py on purpose, so an SDK breakingchange is invisible to them. All 12 passed while the server could not import atall under SDK 2.x, which is what test_server_import.py now guards against.
Roadmap
- Richer discovery for agents — structured tool outputs and MCP resources(attach an archive + its schema card as durable context).
- Upstream inclusion as
life-sciences-sciganticinaws-samples/sample-kiro-power-life-sciencesaslife-sciences-scigantic. - Hosted compute is intentionally not exposed here. Scigantic's notebooks areinteractive (a JupyterLab URL a human opens); handing an external agent that URLis a dead end. The agent-to-agent path is
get_data_access— the caller runs theanalysis in its own environment. Letting Scigantic execute code for an agent(run against an ephemeral kernel with the dataset mounted, return outputs) is aseparate capability the platform would need to build first.
License
MIT-0.
