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 LangChain 10 tools — connect in under 2 minutes

Name: Endorsal Testimonials
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Endorsal Testimonials through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "endorsal-testimonials": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Endorsal Testimonials, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Endorsal Testimonials through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Endorsal Testimonials MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Endorsal Testimonials via MCP

Why Use LangChain with the Endorsal Testimonials MCP Server

LangChain provides unique advantages when paired with Endorsal Testimonials through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Endorsal Testimonials MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Endorsal Testimonials queries for multi-turn workflows

Endorsal Testimonials + LangChain Use Cases

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

RAG with live data: combine Endorsal Testimonials tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Endorsal Testimonials, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Endorsal Testimonials tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Endorsal Testimonials tool call, measure latency, and optimize your agent's performance

Endorsal Testimonials MCP Tools for LangChain (10)

These 10 tools become available when you connect Endorsal Testimonials to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Endorsal Testimonials + LangChain FAQ

Common questions about integrating Endorsal Testimonials MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.