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added MCP example with strands agents
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‎mcp_neo4j_client.py‎

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# mcp_client_example_mistral.py
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from mcp.client.streamable_http import streamablehttp_client
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from strands import Agent
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from strands.tools.mcp.mcp_client import MCPClient
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# IMPORT MODELLO MISTRAL
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from strands.models.mistral import MistralModel
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import os
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MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")
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def create_streamable_http_transport():
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# L’URL deve essere quello del tuo server FastMCP
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return streamablehttp_client("http://localhost:8000/mcp/")
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def main():
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# 1) Crea client MCP
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streamable_http_mcp_client = MCPClient(create_streamable_http_transport)
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with streamable_http_mcp_client:
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# 2) Lista tool MCP
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tools = streamable_http_mcp_client.list_tools_sync()
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print("TOOLS disponibili:", [getattr(t, "name", t.mcp_tool.name) for t in tools])
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# 3) Crea l'agente con Mistral
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agent = Agent(
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tools=tools,
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model=MistralModel(
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api_key=MISTRAL_API_KEY,
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model_id="mistral-small-latest",
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temperature=0.7,
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max_tokens=200,
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),
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)
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# 4) Esegui domanda tramite agente
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response = agent("What is 125 plus 375?")
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print("Risposta agente:", response)
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# 5) Oppure invoca il tool direttamente
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result = streamable_http_mcp_client.call_tool_sync(
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tool_use_id="tool-1",
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name="neo4j_query",
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arguments={"x": 125, "y": 375},
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)
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print("Risultato calcolatore:", result["content"][0]["text"])
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if __name__ == "__main__":
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main()

‎mcp_neo4j_client2.py‎

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from mcp.client.streamable_http import streamablehttp_client
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from mcp.client import MCPClient
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def create_transport():
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return streamablehttp_client("http://localhost:8000/mcp")
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if __name__ == "__main__":
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client = MCPClient(create_transport)
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with client:
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tools = client.list_tools_sync()
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print("TOOLS disponibili dal server MCP:", [t.name for t in tools])
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# Esegui una query di test su Neo4j
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query = "MATCH (n) RETURN n LIMIT 5"
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result = client.call_sync("neo4j_query", query)
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print("RISULTATO QUERY:\n", result)

‎mcp_neo4j_client3.py‎

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# mcp_client_example.py
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from mcp.client.streamable_http import streamablehttp_client
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from strands import Agent
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from strands.tools.mcp.mcp_client import MCPClient
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def create_streamable_http_transport():
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# Nota: l'URL deve corrispondere a quello usato dal tuo server FastMCP
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return streamablehttp_client("http://localhost:8000/mcp/")
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def main():
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# Costruisci il client MCP di Strands
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streamable_http_mcp_client = MCPClient(create_streamable_http_transport)
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# Usa il client in un context manager
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with streamable_http_mcp_client:
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tools = streamable_http_mcp_client.list_tools_sync()
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print("TOOLS disponibili:", [getattr(t, "name", t.mcp_tool.name) for t in tools])
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# Crea l'agente Strands con quegli strumenti
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agent = Agent(tools=tools)
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# Chiedi qualcosa all'agente che usa il tool "add"
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response = agent("What is 125 plus 375?")
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print("Risposta agente:", response)
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# Oppure invoca direttamente il tool
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result = streamable_http_mcp_client.call_tool_sync(
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tool_use_id="tool-1",
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name="add",
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arguments={"x": 125, "y": 375}
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)
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print("Risultato calcolatore:", result["content"][0]["text"])
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if __name__ == "__main__":
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main()

‎mcp_neo4j_mistral_example.py‎

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from strands import Agent
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from mcp.client.streamable_http import streamablehttp_client
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from strands.tools.mcp.mcp_client import MCPClient
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from strands.models.mistral import MistralModel
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def create_streamable_http_transport():
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return streamablehttp_client("http://localhost:8000/mcp/")
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def main():
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mcp_client = MCPClient(create_streamable_http_transport)
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with mcp_client:
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tools = mcp_client.list_tools_sync()
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print("TOOLS disponibili:", [getattr(t, "name", t.mcp_tool.name) for t in tools])
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# Agente Mistral con prompt system che forza l'uso del tool
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agent = Agent(
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tools=tools,
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model=MistralModel(
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api_key="aDHQXyGQcIP4uNSdI6vc24YA8vjkLr22",
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model_id="mistral-small-latest",
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max_tokens=200,
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temperature=0,
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stream=False
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),
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system_prompt=(
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"Se ricevi un'operazione da eseguire, non calcolarla da solo. "
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"Usa sempre il tool Neo4j `neo4j_query` per ottenere il risultato. "
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"Non scrivere numeri direttamente, sempre tramite il tool."
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)
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)
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# Esempio: somma tramite query Neo4j
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query = "RETURN 125 + 375 AS result"
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response = agent(f"Esegui la query: {query}")
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print("Risposta agente:", response)
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if __name__ == "__main__":
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main()

‎mcp_neo4j_mistral_example1.py‎

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# mcp_neo4j_mistral_example.py
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from mcp.client.streamable_http import streamablehttp_client
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from strands import Agent
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from strands.tools.mcp.mcp_client import MCPClient
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from strands.models.mistral import MistralModel
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def create_streamable_http_transport():
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# URL del tuo server FastMCP
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return streamablehttp_client("http://localhost:8000/mcp/")
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def main():
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# 1️⃣ Costruisci il client MCP
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streamable_http_mcp_client = MCPClient(create_streamable_http_transport)
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with streamable_http_mcp_client:
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# 2️⃣ Lista dei tool disponibili
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tools = streamable_http_mcp_client.list_tools_sync()
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print("TOOLS disponibili:", [getattr(t, "name", t.mcp_tool.name) for t in tools])
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# 3️⃣ Crea l'agente con modello Mistral
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agent = Agent(
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tools=tools,
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model=MistralModel(
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api_key="aDHQXyGQcIP4uNSdI6vc24YA8vjkLr22", # <--- metti la tua chiave
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model_id="mistral-small-latest",
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max_tokens=200,
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temperature=0.7,
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)
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)
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# 4️⃣ Esempio: Mistral usa il tool neo4j_query per calcolare 125+375
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# Nota: il prompt deve istruire l'agente a usare il tool
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prompt = """
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Compute 125 + 375 using the neo4j_query tool.
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Only use the tool, do not calculate in your head.
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"""
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response = agent(prompt)
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print("Risposta agente:", response)
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# 5️⃣ Alternativa: chiamata diretta al tool (manuale)
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cypher_query = "RETURN 125 + 375 AS result"
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result = streamable_http_mcp_client.call_tool_sync(
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tool_use_id="tool-1",
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name="neo4j_query",
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arguments={"query": cypher_query}
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)
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print("Risultato Neo4j:", result["content"][0]["text"])
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if __name__ == "__main__":
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main()

‎mcp_neo4j_mistral_example2.py‎

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# mcp_neo4j_mistral_example.py
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from mcp.client.streamable_http import streamablehttp_client
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from strands import Agent
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from strands.tools.mcp.mcp_client import MCPClient
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from strands.models.mistral import MistralModel
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def create_transport():
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# Transport per comunicare col server MCP locale
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return streamablehttp_client("http://localhost:8000/mcp/")
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def main():
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# 1️⃣ Configura client MCP
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client = MCPClient(create_transport)
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with client:
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# Lista tool disponibili
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tools = client.list_tools_sync()
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print("TOOLS disponibili:", [t.mcp_tool.name for t in tools])
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# 2️⃣ Crea l'agente Mistral
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# Nota: aggiungiamo al prompt istruzioni per usare solo i tool
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agent = Agent(
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tools=tools,
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model=MistralModel(
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api_key="aDHQXyGQcIP4uNSdI6vc24YA8vjkLr22", # metti la tua chiave
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model_id="mistral-small-latest",
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max_tokens=200,
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temperature=0.0,
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system_prompt=(
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"You are a Neo4j assistant. "
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"You MUST always use the 'neo4j_query' tool to execute queries. "
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"Never answer arithmetic yourself."
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)
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)
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)
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# 3️⃣ Prompt all'agente
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prompt = """
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You must always use the 'neo4j_query' tool to execute any query.
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Do not answer arithmetic yourself.
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Execute this query in Neo4j: RETURN 125 + 375 AS result
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"""
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# Invocazione dell'agente
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response = agent(prompt)
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print("Risposta agente:", response)
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# 4️⃣ Chiamata diretta al tool Neo4j (solo per verifica)
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neo4j_result = client.call_tool_sync(
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tool_use_id="tool-1",
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name="neo4j_query",
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arguments={"query": "RETURN 125 + 375 AS result"}
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)
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print("Risultato Neo4j:", neo4j_result)
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if __name__ == "__main__":
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main()

‎mcp_neo4j_mistral_example3.py‎

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from mcp.client.streamable_http import streamablehttp_client
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from strands import Agent
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from strands.tools.mcp.mcp_client import MCPClient
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from strands.models.mistral import MistralModel
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def create_streamable_http_transport():
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return streamablehttp_client("http://localhost:8000/mcp/")
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def main():
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# 1) Costruisci il client MCP
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streamable_http_mcp_client = MCPClient(create_streamable_http_transport)
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with streamable_http_mcp_client:
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# 2) Lista tools disponibili
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tools = streamable_http_mcp_client.list_tools_sync()
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print("TOOLS disponibili:", [t.mcp_tool.name for t in tools])
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# 3) Crea l'agente con Mistral
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agent = Agent(
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tools=tools,
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model=MistralModel(
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api_key="aDHQXyGQcIP4uNSdI6vc24YA8vjkLr22",
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model_id="mistral-small-latest",
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max_tokens=200,
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temperature=0.7
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)
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)
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# 4) Prompt strutturato: forza l'uso del tool
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prompt = """
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You must always use the 'neo4j_query' tool to execute any query.
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Do not calculate arithmetic yourself.
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Query to execute: RETURN 125 + 375 AS result
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"""
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response = agent(prompt)
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print("Risposta agente:", response)
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# 5) Chiamata diretta al tool (opzionale)
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result = streamable_http_mcp_client.call_tool_sync(
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tool_use_id="tool-1",
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name="neo4j_query",
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arguments={"query": "RETURN 125 + 375 AS result"}
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)
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print("Risultato Neo4j:", result)
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if __name__ == "__main__":
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main()

‎mcp_neo4j_server-without-neo4j.py‎

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from strands import Agent
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from strands.models.mistral import MistralModel
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from strands.tools.mcp import MCPClient
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import os
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# Import del client HTTP MCP (streamable)
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try:
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from mcp.client.streamable_http import streamablehttp_client
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except ImportError:
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# fallback se la versione mcp è differente
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from mcp.client.http import http_client as streamablehttp_client
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MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")
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def create_transport():
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# Qui indichi l'endpoint HTTP del tuo server MCP
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# Assicurati che il percorso corrisponda a quello usato da FastMCP (es. /mcp)
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return streamablehttp_client("http://localhost:8000/mcp")
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mcp_client = MCPClient(create_transport)
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if __name__ == "__main__":
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with mcp_client:
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tools = mcp_client.list_tools_sync()
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print("TOOLS disponibili dal server MCP:", [t.name for t in tools])
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# Crea l’agente Strands con Mistral
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model = MistralModel(
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api_key=MISTRAL_API_KEY, # metti la tua chiave Mistral
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model_id="mistral-small-latest",
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max_tokens=200,
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temperature=0.7,
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)
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agent = Agent(model=model, tools=tools)
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# Prompt: chiedi di eseguire una query tramite il tool Neo4j
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prompt = "Esegui questa query Neo4j e dimmi il risultato: `MATCH (n) RETURN n LIMIT 5`"
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result = agent(prompt)
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# Stampa il risultato restituito dall'agente
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# a seconda della versione di Strands, può essere result.output o str(result)
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try:
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print("RISULTATO:", result.output)
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except AttributeError:
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print("RISULTATO:", result)

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