Mattermark MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mattermark as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Mattermark. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Mattermark?"
)
print(response)
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.
LlamaIndex agents combine Mattermark tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Mattermark MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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 Mattermark
Why Use LlamaIndex with the Mattermark MCP Server
LlamaIndex provides unique advantages when paired with Mattermark through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mattermark tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mattermark tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mattermark, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mattermark tools were called, what data was returned, and how it influenced the final answer
Mattermark + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mattermark MCP Server delivers measurable value.
Hybrid search: combine Mattermark real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mattermark to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Mattermark for fresh data
Analytical workflows: chain Mattermark queries with LlamaIndex's data connectors to build multi-source analytical reports
Mattermark MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Mattermark to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Mattermark to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMattermark + LlamaIndex FAQ
Common questions about integrating Mattermark MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.