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1 Commits
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 Hermes Pipeline
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5f92123ef2 |
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---
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name: mcp-server-setup
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version: 1.0.0
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description: Set up an MCP server to integrate PipesHub with any MCP-compatible client.
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inputs:
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- MCP server configuration details
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- PipesHub credentials
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steps:
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- 'Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`'
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- 'Step 2: Navigate to the cloned directory with `cd mcp-server`'
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- 'Step 3: Run the interactive installer by executing `./install.sh`'
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- 'Step 4: Follow the prompts in the installer to configure the server, including
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setting up graph DB, message broker, and KV store'
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- 'Step 5: The installer will generate a `.env` file with necessary environment variables.
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Ensure these are correctly set'
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- 'Step 6: Start the MCP server by running `docker-compose up -d`'
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outputs:
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- Running MCP server
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- .env file generated
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tags: []
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metadata:
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source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
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extracted_at: ''
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confidence: 0.95
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---
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# mcp-server-setup
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Set up an MCP server to integrate PipesHub with any MCP-compatible client.
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## Setup
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**Dependencies:**
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```text
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pip install docker docker-compose
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```
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**Setup steps:**
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1. Ensure Docker and Docker Compose are installed on your system.
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1. Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
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## Key Files
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- `path/to/install.sh - Script to run the interactive installer`
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- `path/to/docker-compose.yml - Configuration for Docker services`
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## Steps
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1. Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
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2. Step 2: Navigate to the cloned directory with `cd mcp-server`
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3. Step 3: Run the interactive installer by executing `./install.sh`
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4. Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store
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5. Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set
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6. Step 6: Start the MCP server by running `docker-compose up -d`
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## Implementation Details
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```python
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```bash
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./install.sh
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```
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Run this script to start the installation process.
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```
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```python
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```yaml
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docker-compose:
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version: '3.9'
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services:
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mcp-server:
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image: pipeshubai/mcp-server:latest
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environment:
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- PIPESHUB_API_KEY=your_api_key_here
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```
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This snippet shows how to configure the Docker Compose file.
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```
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## Inputs
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- MCP server configuration details
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- PipesHub credentials
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## Outputs
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- Running MCP server
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- .env file generated
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## Failure Modes
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- Installer fails to run due to missing dependencies or incorrect configuration
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- Docker Compose setup issues preventing server from starting
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## Source
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Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
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Confidence: 0.95
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@@ -1,6 +0,0 @@
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# Commands: mcp-server-setup
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## Available Commands
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- `/skill mcp-server-setup` — Load this skill
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- `/run mcp-server-setup` — Execute workflow
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@@ -1,10 +0,0 @@
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# Examples: mcp-server-setup
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## Usage Example
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```python
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# How to use this skill
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# Inputs: MCP server configuration details, PipesHub credentials
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# Process: Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git` → Step 2: Navigate to the cloned directory with `cd mcp-server` → Step 3: Run the interactive installer by executing `./install.sh`
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# Outputs: Running MCP server, .env file generated
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```
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@@ -1,29 +0,0 @@
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{
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"name": "mcp-server-setup",
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"version": "1.0.0",
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"goal": "Set up an MCP server to integrate PipesHub with any MCP-compatible client.",
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"inputs": [
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"MCP server configuration details",
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"PipesHub credentials"
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],
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"steps": [
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"Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`",
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"Step 2: Navigate to the cloned directory with `cd mcp-server`",
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"Step 3: Run the interactive installer by executing `./install.sh`",
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"Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store",
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"Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set",
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"Step 6: Start the MCP server by running `docker-compose up -d`"
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],
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"outputs": [
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"Running MCP server",
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".env file generated"
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],
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"failure_modes": [
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"Installer fails to run due to missing dependencies or incorrect configuration",
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"Docker Compose setup issues preventing server from starting"
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],
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"confidence": 0.95,
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"explanation": "This workflow is specific but can be adapted for different deployment environments and configurations.",
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"source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
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"score": 1.0
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}
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@@ -0,0 +1,90 @@
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---
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name: research-pipeline
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version: 1.0.0
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description: Fetch a Wikipedia page, summarise its content using an AI agent, and
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write the summary to a file.
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inputs:
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- URL of the Wikipedia page
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steps:
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- 'Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap
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import NIM_MODEL, require_nim_api_key; import blacknode as bn`'
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- 'Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the
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API key is set.'
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- 'Step 3: Create a graph instance: Initialize `g = bn.Graph()`.'
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- 'Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node(''Literal'',
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value=''URL of the Wikipedia page''); fetcher = g.node(''HTTPGet''); summarise =
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g.node(''LLMAgent'', system=''You are a technical writer. Summarise the text in
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3 bullet points.'', model=NIM_MODEL); writer = g.node(''FileWrite'', path=''summary.txt'')`'
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- 'Step 5: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
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fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
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- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
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print(f''Summary written to: {result}'')`'
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outputs:
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- Path of the summary file
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tags: []
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metadata:
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source_repo: https://github.com/temiroff/Blacknode.git
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extracted_at: ''
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confidence: 0.95
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---
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# research-pipeline
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Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.
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## Setup
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**Dependencies:**
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```text
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pip install anthropic>=0.25 docker>=7.1 openai>=1.0
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```
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**Setup steps:**
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1. Ensure NVIDIA NIM API key is set in the environment or editor
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1. Install required dependencies: `pip install -r requirements.txt`
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## Key Files
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- `examples/research_pipeline.py - Contains the research pipeline workflow`
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## Steps
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1. Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`
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2. Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.
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3. Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
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4. Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')`
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5. Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`
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6. Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`
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## Implementation Details
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```python
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from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn
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```
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```python
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url = g.node('Literal', value='https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro=1&explaintext=1&titles=Houdini_(software)&format=json&formatversion=2&origin=*'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')
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```
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```python
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url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')
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```
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## Inputs
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- URL of the Wikipedia page
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## Outputs
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- Path of the summary file
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## Failure Modes
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- If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty
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## Source
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Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
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Confidence: 0.95
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@@ -0,0 +1,6 @@
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# Commands: research-pipeline
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## Available Commands
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- `/skill research-pipeline` — Load this skill
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- `/run research-pipeline` — Execute workflow
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# Examples: research-pipeline
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## Usage Example
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```python
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# How to use this skill
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# Inputs: URL of the Wikipedia page
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# Process: Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn` → Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set. → Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
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# Outputs: Path of the summary file
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```
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@@ -0,0 +1,26 @@
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{
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"name": "research-pipeline",
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"version": "1.0.0",
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"goal": "Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.",
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"inputs": [
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"URL of the Wikipedia page"
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],
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"steps": [
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"Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`",
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"Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.",
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"Step 3: Create a graph instance: Initialize `g = bn.Graph()`.",
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"Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')`",
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"Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`",
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"Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`"
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],
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"outputs": [
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"Path of the summary file"
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],
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"failure_modes": [
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"If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty"
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],
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"confidence": 0.95,
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"explanation": "This workflow can be adapted to fetch and summarise any Wikipedia page or similar content source.",
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"source_repo": "https://github.com/temiroff/Blacknode.git",
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"score": 1.0
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}
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@@ -1,4 +1,4 @@
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# Tests: mcp-server-setup
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# Tests: research-pipeline
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## Test Checklist
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Reference in New Issue
Block a user