Initial commit

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

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+3.12

README.md

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+# Hello World Agent with MCP
+
+A simple demonstration of Tiny Agents using Gradio MCP server and local Ollama.
+
+## What this does
+
+This example creates:
+- **Gradio MCP Server**: A simple server that provides a "hello world" function
+- **Tiny Agent**: An agent that connects to your local Ollama endpoint and can use the MCP server's tools
+
+## Prerequisites
+
+1. **Ollama** running locally at `http://127.0.0.1:11434`
+2. **qwen3:0.6b** model installed in Ollama
+3. **Node.js and npm** for MCP remote connectivity
+
+## Setup
+
+### 1. Install Ollama and the model
+```bash
+# If you haven't already, install Ollama
+# Then pull the model:
+ollama pull qwen3:0.6b
+```
+
+### 2. Install Node.js dependencies
+```bash
+# Install mcp-remote globally
+npm install -g mcp-remote
+```
+
+### 3. Install Python dependencies
+```bash
+# Using uv (recommended)
+uv sync
+
+# Or using pip
+pip install -r requirements.txt
+```
+
+## Usage
+
+### 1. Start Ollama
+Make sure Ollama is running:
+```bash
+ollama serve
+```
+
+### 2. Run the agent
+```bash
+# Using uv
+uv run python main.py
+
+# Or using python directly
+python main.py
+```
+
+### 3. Interact with the agent
+Once started, you can:
+- Type messages to chat with the agent
+- Ask it to use the hello world function (e.g., "Can you greet Alice using your tool?")
+- Type 'quit' to exit
+
+## Example Interaction
+
+```
+🎉 Agent is ready! Type 'quit' to exit.
+==================================================
+
+👤 You: Can you greet Alice using your available tools?
+
+🤖 Agent: I'll use the hello world function to greet Alice for you.
+
+*Agent calls the hello_world_function with name="Alice"*
+
+Hello, Alice! This message comes from the MCP server.
+```
+
+## How it works
+
+1. **Gradio MCP Server**: Creates an MCP-enabled Gradio interface at `http://127.0.0.1:7860`
+2. **MCP Protocol**: The server exposes the `hello_world_function` via MCP
+3. **Tiny Agent**: Connects to both Ollama (for LLM) and the Gradio server (for tools)
+4. **Tool Usage**: The agent can discover and use the hello world function when appropriate
+
+## Troubleshooting
+
+- **"Connection refused"**: Make sure Ollama is running (`ollama serve`)
+- **"Model not found"**: Install the model (`ollama pull qwen3:0.6b`)
+- **"mcp-remote not found"**: Install it with `npm install -g mcp-remote`
+- **Port conflicts**: The Gradio server uses port 7860 by default

data/fastqc.yaml

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+name: fastqc
+description: Run FastQC on sequenced reads
+keywords:
+  - quality control
+  - qc
+  - adapters
+  - fastq
+tools:
+  - fastqc:
+      description: |
+        FastQC gives general quality metrics about your reads.
+        It provides information about the quality score distribution
+        across your reads, the per base sequence content (%A/C/G/T).
+
+        You get information about adapter contamination and other
+        overrepresented sequences.
+      homepage: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
+      documentation: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/
+      licence: ["GPL-2.0-only"]
+      identifier: biotools:fastqc
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'test', single_end:false ]
+    - reads:
+        type: file
+        description: |
+          List of input FastQ files of size 1 and 2 for single-end and paired-end data,
+          respectively.
+output:
+  - html:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'test', single_end:false ]
+      - "*.html":
+          type: file
+          description: FastQC report
+          pattern: "*_{fastqc.html}"
+  - zip:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'test', single_end:false ]
+      - "*.zip":
+          type: file
+          description: FastQC report archive
+          pattern: "*_{fastqc.zip}"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+authors:
+  - "@drpatelh"
+  - "@grst"
+  - "@ewels"
+  - "@FelixKrueger"
+maintainers:
+  - "@drpatelh"
+  - "@grst"
+  - "@ewels"
+  - "@FelixKrueger"

main.py

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+from smolagents import CodeAgent, LiteLLMModel
+from smolagents.tools import ToolCollection
+import gradio as gr
+
+def chat_with_agent(message, history):
+    """Initialize MCP client for each request to avoid connection issues"""
+    try:
+        with ToolCollection.from_mcp(
+            {"url": "https://notredameslab-nf-ontology.hf.space/gradio_api/mcp/sse", "transport": "sse"},
+            trust_remote_code=True  # Acknowledge that we trust this remote MCP server
+        ) as tool_collection:
+            
+            model = LiteLLMModel(
+                model_id="ollama/devstral:latest",
+                api_base="http://localhost:11434",
+            )
+
+            agent = CodeAgent(
+                tools=tool_collection.tools,
+                model=model,
+                additional_authorized_imports=["inspect", "json"]
+            )
+
+            additional_instructions = """
+            ADDITIONAL IMPORTANT INSTRUCTIONS:
+            use the tool "final_answer" in the code block to provide the answer to the user. Prints are only for debugging purposes. So, to give your results concatenate everything you want to print in a single "final_answer" call as such : final_answer(f"your answer here").
+            """
+
+            agent.system_prompt += additional_instructions
+
+            result = agent.run(message)
+            return str(result)
+            
+    except Exception as e:
+        return f"❌ Error: {e}\nType: {type(e).__name__}"
+
+if __name__ == "__main__":
+    demo = gr.ChatInterface(
+        fn=chat_with_agent,
+        type="messages",
+        examples=["can you extract input/output metadata from fastqc nf-core module ?"],
+        title="Agent with MCP Tools (Per-Request Connection)",
+        description="This version creates a new MCP connection for each request."
+    )
+
+    demo.launch() 

pyproject.toml

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+[project]
+name = "agent-ontology"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "fastmcp>=2.6.1",
+    "gradio[mcp]>=5.0.0",
+    "huggingface_hub[mcp]>=0.32.2",
+    "mcp>=1.9.2",
+    "requests",
+    "smolagents[litellm,mcp]>=1.17.0",
+    "textblob>=0.19.0",
+]