Flowproof MCP
Run reproducible Nextflow bioinformatics pipelines with verifiable Workflow Run RO-Crate provenance: container digests, tool versions and checksums.
التثبيت
خطوات التثبيت موجودة في التوثيق أدناه. التوثيق
التوثيق
مأخوذ من ملف README للمشروع على GitHub ويُحدَّث أسبوعيًا. تفتح الروابط على GitHub.
المحتويات
FlowProof
Reproducible bioinformatics pipeline execution over the Model Context Protocol (MCP), with verifiable provenance.
FlowProof lets an AI assistant run a bioinformatics pipeline and hand back results whose provenance can be independently verified: pipeline version, container digests, tool versions, parameters, and SHA-256 checksums of every input and output, emitted as a Workflow Run RO-Crate.
It fills the layer the MCP-for-bioinformatics ecosystem is missing. Data access already has MCP servers (BioMCP); analysis planning already has AI agents (Biomni, AutoBA). The gap is reliable, trustworthy execution, which needs orchestration and provenance discipline. That is what FlowProof provides.
Why it exists
An AI that "runs an analysis for you" is only useful if you can trust the result. FlowProof makes every run reproducible and independently checkable, so an AI-driven result is not a black box: it ships with the exact recipe and checksums to reproduce it byte-for-byte.
Status
FlowProof is working and installable today (pip install flowproof-mcp). The pipeline registry, execution backends, and RO-Crate provenance are complete and covered by tests, and the MCP server exposes six tools (list, describe, run, status, results, provenance). It ships with two execution backends: a zero-dependency backend for development and CI, and a Nextflow backend that runs real pipelines.
Install
uv sync
Using FlowProof
FlowProof works two ways. Both let an AI assistant run pipelines for you; you never touch a terminal after setup.
Local (recommended for real data)
Runs on your own machine, so your data never leaves it and your compute runs the pipelines. No token needed.
Add this to your MCP client config (Claude Desktop: claude_desktop_config.json; Cursor: MCP settings):
{
"mcpServers": {
"flowproof": {
"command": "uvx",
"args": ["flowproof-mcp"]
}
}
}
Then just ask your assistant: "list the FlowProof pipelines" or "run the ONT assembly on this file". Runs are written under ~/.flowproof/runs (override with FLOWPROOF_RUNS_DIR).
Cloud (instant, no install)
Connect your client to the hosted server. Nothing to install; the cloud runs it. Uses a bearer token today (per-user keys and OAuth are on the roadmap):
{
"mcpServers": {
"flowproof": {
"url": "https://flowproof.specvista.com/mcp/",
"headers": { "Authorization": "Bearer YOUR_TOKEN" }
}
}
}
Run the server directly
flowproof # stdio (local, default)
FLOWPROOF_TRANSPORT=http flowproof # HTTP server on :8000
Tools
| Tool | Purpose |
|---|---|
list_pipelines | Registered pipelines with id, description, read type |
describe_pipeline | Inputs, parameters, outputs for a pipeline |
run_pipeline | Execute a pipeline reproducibly, returns a run id |
get_run_status | Status of a run |
get_results | Output file manifest with checksums |
get_provenance | The verifiable Workflow Run RO-Crate record |
Pipelines (seed)
| id | Read type | Analysis |
|---|---|---|
ont-read-stats | long | Oxford Nanopore read statistics (QC); optional Flye assembly. Runs on a built-in sample if you provide no input. |
variant-call-short | short | Short-read QC to germline variant calling (nf-core/sarek) |
rnaseq | short | RNA-seq quantification: QC, trimming, alignment, gene counts (nf-core/rnaseq) |
fetchngs | short | Fetch raw reads and metadata from public archives, SRA/ENA/GEO (nf-core/fetchngs) |
viralrecon | short | Viral genome reconstruction and variant calling from amplicon data (nf-core/viralrecon) |
New pipelines register by manifest; the server does not change.
Try it instantly: ask your assistant to "run ont-read-stats" with no input, and FlowProof runs the bundled Oxford Nanopore sample end to end and returns a verifiable provenance crate, no data or reference genome required.
Backends
MockBackend: deterministic, dependency-free. Used for development and tests.NextflowBackend: runsnextflow runwith a container profile. Requires Nextflow and Docker.
Provenance
Every run emits ro-crate-metadata.json following the Workflow Run RO-Crate profile, capturing the workflow and version, container images, resolved tool versions, exact parameters, and SHA-256 checksums of all inputs and outputs.
Develop
PYTHONPATH=src uv run --with pytest --no-project python -m pytest tests/ -q
Architecture
See DESIGN.md.
Cite
If you use FlowProof in your research, please cite the archived software release:
Ajibade, H. A. (2026). FlowProof: reproducible bioinformatics pipeline execution over the Model Context Protocol with verifiable provenance (v0.1.2). Zenodo. https://doi.org/10.5281/zenodo.21932977
A machine-readable CITATION.cff is included, so GitHub shows a "Cite this repository" button with BibTeX and APA formats.
@software{flowproof,
author = {Ajibade, Hammed Adedapo},
title = {FlowProof: reproducible bioinformatics pipeline execution over the Model Context Protocol with verifiable provenance},
year = {2026},
version = {0.1.2},
publisher = {Zenodo},
doi = {10.5281/zenodo.21932977},
url = {https://github.com/ajibadedapo/flowproof-mcp}
}
الأسئلة الشائعة
ما هو Flowproof MCP؟
Run reproducible Nextflow bioinformatics pipelines with verifiable Workflow Run RO-Crate provenance: container digests, tool versions and checksums.
كيف أثبّت Flowproof MCP؟
اتبع خطوات التثبيت في توثيقه المعروض في هذه الصفحة وفي مستودعه.
هل Flowproof MCP مفتوح المصدر؟
نعم. شيفرته متاحة على GitHub بترخيص MIT.
من يصون Flowproof MCP؟
ينشره ajibadedapo. يدرجه AI.tn لكنه لا يصونه ولا يوصي به.
من القوائم المجتمعية awesome-mcp-servers ومستودع المشروع على GitHub. الإدراج ليس توصية: راجع شيفرة الخادم وصلاحياته قبل منحه الوصول إلى بياناتك.