How to Use the Mattermark MCP in LangChain

Q: How does LangChain handle tool errors?

If a call to getcompanydetails fails, the agent receives a standard error message. You can configure your chain to catch these or try a different search parameter.

Build complex reasoning chains with Mattermark data directly in your LangChain agents.

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Connect Mattermark MCP to LangChain

Create your Vinkius account to connect Mattermark 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 Mattermark with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-step intelligence chains

Connect `search_companies` directly to `get_company_funding_rounds` within your LangChain pipeline. Your agent handles the logic, pulling data from one tool to trigger the next automatically. Stop writing manual scripts for data extraction. Your chains now execute precise lookups based on real-time startup signals, feeding results straight into your LLM.

Traceable agentic workflows

Every call to `get_investor_details` or `list_similar_companies` shows up in your LangSmith traces. You see exactly what the agent requested and why. Debug your reasoning loops with full visibility into tool inputs. You'll catch logic errors before they hit your production data pipeline.

Composable data aggregation

Combine Mattermark tools with your existing databases in a single LangChain agent. This MCP server acts as just another node in your graph. Aggregate disparate data sources without custom boilerplate. Your agent decides which tool to call based on the specific investment thesis you're currently testing.

Setup guide

Set up Mattermark 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 Mattermark 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({
    "mattermark-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 Mattermark 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 Mattermark. 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 Mattermark MCP in LangChain

You pass the tools directly into your agent definition. Use the LangChain MCP adapter to register the server, then call `get_tools()` to expose the endpoints to your agent.

Yes, you manage concurrency within your agent's execution policy. LangChain lets you wrap these calls in rate-limiting middleware or custom retry logic.

No. The server fetches raw data from the API and passes it through the MCP protocol to your agent. LangChain processes the output in memory without persisting your proprietary startup intelligence.

It does. Every tool is designed for async execution, making it compatible with non-blocking LangChain runtimes.

If a call to `get_company_details` fails, the agent receives a standard error message. You can configure your chain to catch these or try a different search parameter.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript