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This example demonstrates how to use OpenAI's Model Context Protocol (MCP) integration with the Responses API in promptfoo.
You can run this example with:
npx promptfoo@latest init --example openai-mcp
Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to LLMs. OpenAI's MCP integration allows models to use remote MCP servers to perform tasks like searching repositories, accessing APIs, and more.
This example requires the following environment variables:
OPENAI_API_KEY
- Your OpenAI API key from the OpenAI platformSTRIPE_API_KEY
- Your Stripe API key (for authenticated examples only)You can set these in a .env
file or directly in your environment.
Set up environment variables:
export OPENAI_API_KEY="your_openai_api_key"
export STRIPE_API_KEY="your_stripe_api_key" # For authenticated example only
Run individual examples:
# Basic MCP integration
npx promptfoo eval -c promptfooconfig.yaml
# Authenticated MCP servers
npx promptfoo eval -c promptfooconfig.authenticated.yaml
# Approval workflow examples
npx promptfoo eval -c promptfooconfig.approval.yaml
View results:
npx promptfoo view
promptfooconfig.yaml
)Demonstrates basic MCP integration using the public DeepWiki MCP server to search GitHub repositories.
promptfooconfig.authenticated.yaml
)Shows how to use MCP servers that require authentication, using Stripe as an example.
promptfooconfig.approval.yaml
)Demonstrates different approval settings for MCP tool usage.
tools:
- type: mcp
server_label: deepwiki
server_url: https://mcp.deepwiki.com/mcp
require_approval: never
tools:
- type: mcp
server_label: stripe
server_url: https://mcp.stripe.com
headers:
Authorization: 'Bearer ${STRIPE_API_KEY}'
require_approval: never
tools:
- type: mcp
server_label: deepwiki
server_url: https://mcp.deepwiki.com/mcp
allowed_tools: ['ask_question', 'read_wiki_structure']
require_approval: never
tools:
- type: mcp
server_label: deepwiki
server_url: https://mcp.deepwiki.com/mcp
require_approval:
never:
tool_names: ['ask_question']
The examples demonstrate assertion patterns for validating MCP tool interactions:
assert:
- type: is-valid-openai-tools-call # Validates both function and MCP tools
assert:
- type: contains
value: 'MCP Tool Result' # Verify MCP tools were used
- type: not-contains
value: 'MCP Tool Error' # Ensure no MCP errors occurred
assert:
- type: is-valid-openai-tools-call
weight: 0.4
- type: contains-any
value: ['expected', 'content']
weight: 0.3
- type: llm-rubric
value: 'Response quality criteria'
weight: 0.3
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Browsing data directories saved to S3 is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
promptfoo is now integrated with AWS S3!
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Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
promptfoo is now integrated with Google Cloud Storage!
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Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
promptfoo is now integrated with Azure Cloud Storage!
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Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!
promptfoo is now integrated with your S3 compatible storage!
Are you sure you want to delete this access key?