How to Use the Better Proposals MCP in LangChain

Q: How do I connect the Better Proposals MCP Server to LangChain?

Install langchain-mcp-adapters and langgraph. Pass your Vinkius endpoint token to MultiServerMCPClient, call client.gettools(), and feed them to your agent.

Q: Can my LangChain agent automatically create Better Proposals contacts?

Yes. The agent uses the createcontact tool when it detects a missing recipient in your pipeline. It handles the schema mapping automatically.

Build multi-step proposal generation pipelines directly in LangChain.

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Connect Better Proposals MCP to LangChain

Create your Vinkius account to connect Better Proposals to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Better Proposals with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Proposal Creation and Analytics

This MCP Server exposes ten Better Proposals endpoints to your ReAct agents. Your LangChain agent grabs active templates using `list_templates` and immediately passes that context to a drafting step. You build the exact sequence. A chain might trigger `get_company` to pull client details, merge them into a draft, and execute `create_proposal` without human intervention. LangSmith tracks every token and API call along the way.

LangChain MCP Server Integration

Connect to Vinkius via the `MultiServerMCPClient` adapter. Your pipeline gains instant access to `list_contacts` and `create_contact` tools. The agent decides which tool fires based on intermediate results. If a contact is missing during a document build, the chain halts, creates the record, and resumes the proposal flow.

Pipeline-Driven Sales Documents

Stop manually copying CRM data into proposal software. Your agent pulls open deals, cross-references them with `list_proposals`, and identifies missing documents. When it spots a gap, it calls `get_analytics` on past successful pitches to format the new one. The entire workflow happens in a single composable graph.

Setup guide

Set up Better Proposals MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Better Proposals tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "better-proposals-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Better Proposals transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Better Proposals. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Better Proposals MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph`. Pass your Vinkius endpoint token to `MultiServerMCPClient`, call `client.get_tools()`, and feed them to your agent.

Yes. The agent uses the `create_contact` tool when it detects a missing recipient in your pipeline. It handles the schema mapping automatically.

Every tool execution logs latency, input arguments, and API responses. You see exactly what data went into `create_proposal` and how long the Vinkius sandbox took to process it.

Your ReAct agent will fail the current step. It typically calls `list_templates` first to verify availability before attempting document generation.

Vinkius runs this connection in an ephemeral V8 isolate sandbox. Your proposal drafts and viewer metrics pass through a zero-trust environment that shuts down immediately after execution.

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