Mattermark MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Mattermark through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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({
"mattermark": {
"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 Mattermark, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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 Mattermark MCP Server
Connect your Mattermark account to any AI agent and access deep insights into the startup ecosystem through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Mattermark through native MCP adapters. Connect 10 tools via 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
- Company Research — Search for startups, fetch detailed metadata, and monitor funding history
- Investor Intelligence — List venture firms and inspect their portfolios and profiles
- Funding Rounds — Query specific investment rounds and their details
- Competitive Analysis — Find similar companies and track employee growth trends
The Mattermark 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 Mattermark to LangChain via MCP
Follow these steps to integrate the Mattermark 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 Mattermark via MCP
Why Use LangChain with the Mattermark MCP Server
LangChain provides unique advantages when paired with Mattermark through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Mattermark 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 Mattermark queries for multi-turn workflows
Mattermark + LangChain Use Cases
Practical scenarios where LangChain combined with the Mattermark MCP Server delivers measurable value.
RAG with live data: combine Mattermark tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Mattermark, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Mattermark tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Mattermark tool call, measure latency, and optimize your agent's performance
Mattermark MCP Tools for LangChain (10)
These 10 tools become available when you connect Mattermark to LangChain via MCP:
get_company_details
Get details for a specific company
get_company_employees
Get employee data for a company
get_company_funding_rounds
Get funding history for a company
get_company_news
Get news for a specific company
get_funding_round_details
Get details for a funding round
get_investor_details
Get details for an investor
list_investors
List venture capital investors
list_similar_companies
Find similar companies
search_companies
Search for companies
search_funding_rounds
Search for funding rounds
Example Prompts for Mattermark in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Mattermark immediately.
"Search for companies in the 'Fintech' sector in New York."
"Get funding history for company ID 123."
"List similar companies to 'Stripe'."
Troubleshooting Mattermark MCP Server with LangChain
Common issues when connecting Mattermark to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersMattermark + LangChain FAQ
Common questions about integrating Mattermark MCP Server with LangChain.
How does LangChain connect to MCP servers?
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?
Can I trace MCP tool calls in LangSmith?
Connect Mattermark 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.
AI-first code editor with integrated LLM-powered coding assistance.
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 Mattermark to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.