Allows running the MCP server in a containerized environment with volume mounting to access workspace pipelines.
Enables cloning and managing nf-core pipeline repositories to make them available for the MCP server to interact with.
Provides tools for managing and navigating nf-core pipeline repositories, including listing pipelines, accessing configurations and workflows, searching through pipeline files, and exploring pipeline modules.
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., "@nf-core MCP Server list-pipelines"
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.
An MCP server for managing and navigating nf-core pipeline repositories.
List local nf-core repositories (rnaseq, sarek, modules, tools)
Access pipeline configurations and workflows
Search through pipeline files
Explore pipeline modules
Related MCP server: pipen-mcp
cd nf-core_mcp
npm install
# Build TypeScript
npm run build
# Start the server
npm start# Build the Docker image
cd nf-core_mcp
docker build -t nf-core-mcp .
# Run the container
docker run -i --rm \
-v "/path/to/your/workspace:/app/workspace" \
nf-core-mcpTo add new nf-core pipeline repositories to the workspace:
Clone the repositories:
# Navigate to your workspace directory (example for Windows)
cd /path/to/your/workspace
# Clone desired nf-core repositories
git clone https://github.com/nf-core/rnaseq.git
git clone https://github.com/nf-core/sarek.git
git clone https://github.com/nf-core/modules.git
# Add any other nf-core pipeline you want to manageDirectory Structure: Your workspace should look like this:
workspace/
├── rnaseq/
├── sarek/
├── modules/
└── your-new-pipeline/Verify Installation:
After starting the MCP server, use the list-pipelines command to verify that your new pipelines are detected:
list-pipelinesNote: The MCP server will automatically detect and manage any nf-core pipeline repositories in your workspace directory.
list-pipelines
Lists all nf-core pipelines in the workspace
Shows configuration file status
No parameters required
get-pipeline-modules
Gets module information from a pipeline
Parameters:
pipeline: Pipeline name (rnaseq, sarek, or modules)
search-pipelines
Searches through pipeline files
Parameters:
query: Search query
pipeline (optional): Specific pipeline to search
pipeline-config
Gets pipeline configuration
URI format: pipeline://{name}/config
Parameters:
name: Pipeline name (rnaseq, sarek, or modules)
pipeline-workflow
Gets pipeline workflow
URI format: pipeline://{name}/workflow
Parameters:
name: Pipeline name (rnaseq, sarek, or modules)
Add the following to your mcp.json:
{
"mcpServers": {
"nf-core": {
"command": "npx",
"args": ["-y", "nf-core-mcp"]
}
}
}Add the following to your mcp.json:
{
"mcpServers": {
"nf-core": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-v",
"/path/to/your/workspace:/app/workspace",
"nf-core-mcp"
]
}
}
}Using the MCP server in Cursor:
# List available pipelines
list-pipelines
# Get modules from rnaseq pipeline
get-pipeline-modules pipeline=rnaseq
# Search in all pipelines
search-pipelines query="fastqc"
# Search in specific pipeline
search-pipelines query="fastqc" pipeline=rnaseq
# Access pipeline configuration
pipeline://rnaseq/config
# Access workflow
pipeline://rnaseq/workflow# If installed globally
nf-core-mcp
# If installed locally
npx nf-core-mcp
# Using npx without installation
npx -y nf-core-mcpdocker run -it --rm \
-v /path/to/your/workspace:/app/workspace \
nf-core-mcp# Install dependencies
npm install
# Build TypeScript
npm run build
# Run in development mode
npm run dev
# Run tests
npm test
# Run linter
npm run lintMIT
| Name | Required | Description | Default |
|---|---|---|---|
| pipeline | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
| pipeline | No | ||
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get-pipeline-moduleslist-pipelinessearch-pipelinesThe three tools have clearly distinct purposes: get-pipeline-modules retrieves modules for a specific pipeline, list-pipelines enumerates available pipelines, and search-pipelines finds pipelines based on criteria. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb-noun pattern with hyphens (e.g., get-pipeline-modules, list-pipelines, search-pipelines). The naming is uniform and predictable, using clear action verbs paired with relevant nouns.
With only three tools, the server feels thin for a pipeline management domain, potentially lacking operations like create, update, or delete pipelines. However, the tools cover basic discovery and inspection, which might be sufficient for a limited scope.
The toolset is severely incomplete for pipeline management, missing essential CRUD operations such as creating, updating, or deleting pipelines. It only supports discovery and inspection, leaving significant gaps that could hinder agent workflows.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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