FlowProof
FlowProof is an MCP server that enables AI assistants to reproducibly execute bioinformatics pipelines with verifiable provenance. You can:
List pipelines: Retrieve registered pipelines with IDs, descriptions, and read types (e.g., short-read, ONT long-read).
Describe a pipeline: Get detailed inputs, parameters, and outputs for a specific pipeline.
Run a pipeline: Launch a pipeline with inputs and parameters, receiving a run ID for tracking.
Check run status: Query the current state (running, completed, failed) of a run.
Retrieve results: Obtain a manifest of output files and their SHA-256 checksums.
Get provenance: Download a Workflow Run RO-Crate record capturing workflow version, container digests, tool versions, exact parameters, and all input/output checksums for independent verification.
Enables execution of bioinformatics pipelines using Nextflow as the workflow backend, providing reproducible run management and verifiable provenance.
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., "@FlowProofrun the ONT assembly on sample.fastq"
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.
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.
Related MCP server: pipen-mcp
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 syncUsing 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 :8000Tools
Tool | Purpose |
| Registered pipelines with id, description, read type |
| Inputs, parameters, outputs for a pipeline |
| Execute a pipeline reproducibly, returns a run id |
| Status of a run |
| Output file manifest with checksums |
| The verifiable Workflow Run RO-Crate record |
Pipelines (seed)
id | Read type | Analysis |
| short | Short-read QC to germline variant calling |
| long | Oxford Nanopore long-read de novo assembly |
New pipelines register by manifest; the server does not change.
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/ -qArchitecture
See DESIGN.md.
Maintenance
Related MCP Servers
- AlicenseCqualityBmaintenanceEnables workflow management and Docker image building for Bio-OS platform. Supports WDL workflow submission, validation, and monitoring, along with Docker image building and status tracking for bioinformatics workflows.225MIT
- Alicense-qualityBmaintenanceExposes pipen bioinformatics pipelines as MCP tools, allowing AI assistants to discover and run complex workflows through a progressive disclosure interface.1MIT
- Alicense-qualityCmaintenanceMCP-native scientific skills for reproducible computational biology and AI-driven drug-discovery workflows. It combines deterministic scientific tools with an MCP server to give AI agents real computational capabilities.Apache 2.0
- Alicense-qualityDmaintenanceEnables AI assistants to programmatically create, manage, and orchestrate n8n workflows through a standardized MCP interface.MIT
Related MCP Connectors
MCP Hub: AI service discovery, per-user OAuth, and multi-service workflow orchestration
Create, browse, remix, collaborate on, and run durable AI workflow nodes from MCP hosts.
AI-powered bioprotocol optimization — generate, search, and manage lab protocols via MCP
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ajibadedapo/flowproof-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server