CustomerGauge MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect CustomerGauge through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to CustomerGauge "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in CustomerGauge?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About CustomerGauge MCP Server
Integrate CustomerGauge, the leading B2B Experience Management platform, directly into your AI workflow. Monitor customer survey responses, track Net Promoter Scores (NPS) across your account portfolio, and analyze the revenue impact of customer experience using natural language.
Pydantic AI validates every CustomerGauge tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Response Monitoring — List and retrieve full details for customer survey responses and feedback.
- Account NPS Tracking — Monitor NPS metrics for specific business accounts and business units.
- Contact Insights — Access detailed profiles and survey history for individual account contacts.
- Revenue Impact Analysis — List revenue data associated with accounts to understand experience-driven growth.
The CustomerGauge MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect CustomerGauge to Pydantic AI via MCP
Follow these steps to integrate the CustomerGauge MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from CustomerGauge with type-safe schemas
Why Use Pydantic AI with the CustomerGauge MCP Server
Pydantic AI provides unique advantages when paired with CustomerGauge through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your CustomerGauge integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your CustomerGauge connection logic from agent behavior for testable, maintainable code
CustomerGauge + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the CustomerGauge MCP Server delivers measurable value.
Type-safe data pipelines: query CustomerGauge with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple CustomerGauge tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query CustomerGauge and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock CustomerGauge responses and write comprehensive agent tests
CustomerGauge MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect CustomerGauge to Pydantic AI via MCP:
get_account_nps
Resolves quantitative satisfaction scores. Interacts with the sentiment aggregation engine. Get the Net Promoter Score (NPS) for a specific account
get_business_unit_nps
Resolves organizational performance data. Interacts with the business unit hierarchy. Get NPS metrics for a specific business unit
get_contact_profile
Resolves interaction history and individual sentiment trends. Interacts with the customer lifecycle boundary. Get detailed profile and survey history for a contact
get_portfolio_nps_summary
Resolves global experience metrics. Touches the executive reporting boundary. Get an overall NPS summary across your entire account portfolio
get_response_details
Resolves verbatim comments, respondent metadata, and driver scores. Touches the granular feedback analytics boundary. Get full details for a specific survey response
list_account_contacts
Resolves contact identifiers and associated account links. Touches the CRM and relationship boundary. List contacts associated with your business accounts
list_b2b_accounts
Resolves account IDs, names, and organizational mappings. Touches the account management and segmentation boundary. List all business accounts managed in CustomerGauge
list_revenue_impact_data
Resolves monetary values and account associations for ROI calculation. Touches the financial data integration boundary. List revenue data associated with accounts for experience impact analysis
list_survey_responses
Resolves response IDs, scores (NPS), and timestamp data. Interacts with the survey response repository. List all customer survey responses in CustomerGauge
search_responses_by_keyword
Resolves feedback entries matching the query keyword. Touches the indexed text search boundary. Search through survey comments and feedback by keyword
Example Prompts for CustomerGauge in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with CustomerGauge immediately.
"List all survey responses received this morning."
"What is the current NPS for account 'Global Logistics'?"
"Search for feedback containing the word 'pricing'."
Troubleshooting CustomerGauge MCP Server with Pydantic AI
Common issues when connecting CustomerGauge to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiCustomerGauge + Pydantic AI FAQ
Common questions about integrating CustomerGauge MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect CustomerGauge with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect CustomerGauge to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.