How to Use the Mattermark MCP in LlamaIndex

Q: Can I filter Mattermark tools in LlamaIndex?

Yes, use the allowedtools filter when setting up your agent. This restricts the agent to only use the functions you explicitly authorize.

Index startup intelligence from Mattermark into your RAG applications with LlamaIndex.

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Connect Mattermark MCP to LlamaIndex

Create your Vinkius account to connect Mattermark to LlamaIndex 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 LlamaIndex

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Perplexity

Semantic indexing of startup data

Turn `get_company_news` and `get_company_employees` results into searchable vectors. LlamaIndex stores this data so you can query it with natural language later. Build a knowledge base that grows with every API call. Your application stops being a simple wrapper and starts acting like a persistent research assistant.

Grounded AI responses

Force your agent to use `get_funding_round_details` as the source of truth. Your RAG app pulls exact figures instead of hallucinating valuations. Ground your insights in verified venture capital data. Your users get precise answers backed by the raw intelligence provided by the tools.

Query past research sessions

Index the results of your `search_companies` queries to build a history of your market scanning. You can ask your agent about companies you vetted last week. Keep your research organized in a vector store. The data stays fresh and accessible whenever you need to revisit a specific investment lead.

Setup guide

Set up Mattermark MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Mattermark MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Mattermark tools.",
)
response = await agent.run("List recent Mattermark data")

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Common questions about Mattermark MCP in LlamaIndex

Use the MCP tool spec to convert server functions into LlamaIndex tools. You then pass these into a FunctionAgent to enable autonomous data retrieval.

The framework indexes it into your vector store. This allows you to perform semantic searches over the results returned by the server.

Your indexes are local to your environment. The MCP server facilitates the transfer, but your vector store remains private and under your control.

Yes, use the allowed_tools filter when setting up your agent. This restricts the agent to only use the functions you explicitly authorize.

Data privacy is maintained by keeping all retrieved records within your local memory or persistent vector store. No external party touches the specific company or funding data you pull.

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