How do I get an Endorsal API Key?

Log in to your Endorsal dashboard, navigate to **Account > API**, and you can generate or retrieve your unique API Key from there. Ensure you have an active plan that supports API access.

Can the agent approve testimonials?

Yes, you can use the approve_pending_testimonial tool to change the status of any pending review to 'Approved' directly via the agent.

Does the integration show customer photos?

You can retrieve testimonial metadata using the get_testimonial_details tool. While it provides image URLs and details, the actual photos are best viewed within the Endorsal dashboard or live widgets.

Endorsal Testimonials MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Endorsal Testimonials
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Endorsal Testimonials through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Endorsal Testimonials "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Endorsal Testimonials?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Endorsal Testimonials MCP Server

Integrate Endorsal, the fully automated testimonial collection platform, directly into your AI workflow. Manage your collected testimonials and customer ratings, track display widgets and website properties, monitor pending reviews and approval statuses, and oversee your social proof using natural language.

Pydantic AI validates every Endorsal Testimonials tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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

Testimonial Oversight — List and retrieve detailed content, customer ratings, and photos for all your collected testimonials.
Widget Intelligence — Monitor display widgets and properties, resolving widget types and deployment identifiers across your brands.
Approval Management — Access and approve pending testimonials, ensuring high-quality social proof is published instantly.
Social Proof Auditing — Retrieve high-level summaries of review volumes, widget activity, and organizational social proof health instantly.

The Endorsal Testimonials 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 Endorsal Testimonials to Pydantic AI via MCP

Follow these steps to integrate the Endorsal Testimonials 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 Endorsal Testimonials with type-safe schemas

Why Use Pydantic AI with the Endorsal Testimonials MCP Server

Pydantic AI provides unique advantages when paired with Endorsal Testimonials 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 Endorsal Testimonials integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Endorsal Testimonials connection logic from agent behavior for testable, maintainable code

Endorsal Testimonials + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Endorsal Testimonials MCP Server delivers measurable value.

Type-safe data pipelines: query Endorsal Testimonials with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Endorsal Testimonials tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Endorsal Testimonials and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Endorsal Testimonials responses and write comprehensive agent tests

Endorsal Testimonials MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Endorsal Testimonials to Pydantic AI via MCP:

approve_pending_testimonial

Approve a pending testimonial for public display

get_endorsal_account_metadata

Retrieve metadata and limits for your Endorsal account

get_testimonial_details

Get full content and metadata for a specific testimonial

list_account_properties

List all properties (websites/brands) managed in your account

list_all_testimonials

List all testimonials collected in your Endorsal account

list_display_widgets

g. wall of love, badge), and unique identifiers. List all display widgets configured in your account

list_latest_testimonials

Identify the most recently collected testimonials

list_pending_testimonials

Identify testimonials that are currently awaiting approval

quick_social_proof_audit

Retrieve a high-level summary of testimonials and widget activity

search_testimonials_by_keyword

Search for testimonials using a customer name or testimonial keyword

Example Prompts for Endorsal Testimonials in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Endorsal Testimonials immediately.

"List all my collected testimonials."

"Show me the display widgets configured."

"Approve testimonial ID 'TEST-12345'."

Troubleshooting Endorsal Testimonials MCP Server with Pydantic AI

Common issues when connecting Endorsal Testimonials to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Endorsal Testimonials + Pydantic AI FAQ

Common questions about integrating Endorsal Testimonials MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an 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?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Endorsal Testimonials MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.