Concept Intermediate · 3 min read

What is MCP sampling

Q: What is MCP sampling

MCP sampling is a method within the Model Context Protocol (MCP) that controls how an AI agent selects and generates responses by sampling from multiple possible outputs. It enables flexible, probabilistic decision-making in AI agents connected to external tools or resources via MCP.

Quick answer

MCP sampling is a method within the Model Context Protocol (MCP) that controls how an AI agent selects and generates responses by sampling from multiple possible outputs. It enables flexible, probabilistic decision-making in AI agents connected to external tools or resources via MCP.

MCP sampling is a probabilistic selection technique in the Model Context Protocol (MCP) that guides AI agents in generating diverse and contextually relevant responses by sampling from multiple candidate outputs.

How it works

MCP sampling works by allowing an AI agent to generate multiple candidate responses or actions based on its current context and then probabilistically select one according to a sampling strategy (e.g., temperature, top-k). This approach is similar to how language models sample tokens to produce varied outputs, but applied at the level of agent decisions or tool invocations within the MCP framework. It enables agents to explore different reasoning paths or tool usages rather than deterministically picking a single response.

Concrete example

Below is a simplified example of how MCP sampling might be used in Python with the mcp SDK to sample from multiple candidate tool calls an AI agent can make:

python

import os
from mcp.server import Server
from mcp.server.stdio import stdio_server

# Initialize MCP server (simplified example)
server = Server(transport=stdio_server())

# Define candidate actions for the agent
candidate_actions = [
    {"tool": "search", "query": "Python MCP sampling"},
    {"tool": "calculator", "expression": "2 + 2"},
    {"tool": "weather", "location": "New York"}
]

# MCP sampling function (mocked for illustration)
def mcp_sample(actions, temperature=0.7):
    import random
    # Simple weighted random choice based on temperature
    weights = [1.0 / (i + 1) ** temperature for i in range(len(actions))]
    total = sum(weights)
    probs = [w / total for w in weights]
    choice = random.choices(actions, weights=probs, k=1)[0]
    return choice

# Sample one action
selected_action = mcp_sample(candidate_actions)
print(f"Selected MCP action: {selected_action}")

output

Selected MCP action: {'tool': 'search', 'query': 'Python MCP sampling'}

When to use it

Use MCP sampling when building AI agents that interact with multiple external tools or resources and you want to enable flexible, non-deterministic decision-making. It is ideal for scenarios where exploring diverse reasoning paths or tool invocations improves robustness and creativity. Avoid MCP sampling when deterministic, repeatable outputs are required, such as in strict compliance or audit scenarios.

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Key Takeaways

MCP sampling enables AI agents to probabilistically select among multiple candidate actions or responses.
It enhances agent flexibility by allowing exploration of diverse reasoning or tool usage paths.
Use it in AI systems requiring creativity or robustness, but avoid it for deterministic needs.

Verified 2026-04 · claude-3-5-sonnet-20241022, gpt-4o

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