How to Use the Common Paper MCP in LlamaIndex

Q: Does Common Paper work with LlamaIndex filters?

You can use an allowedtools filter to restrict which actions the agent performs. This keeps your RAG application focused on the specific contract data you need.

Index your Common Paper contract data into LlamaIndex for grounded, searchable legal knowledge.

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Connect Common Paper MCP to LlamaIndex

Create your Vinkius account to connect Common Paper 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 Common Paper with LlamaIndex

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Index agreements into LlamaIndex

Map the output of `list_agreements` directly into your vector store. Your knowledge base updates whenever you pull fresh data from your account, ensuring your search results reflect current contract statuses. This approach turns static API responses into queryable information. You can ask your agent about specific contract terms and get answers grounded in the actual metadata returned by the server.

Retrieve contract templates for RAG

Use `list_templates` to populate your index with available document formats. When you need to draft a new contract, your agent searches the index to find the correct template ID. Combine this with `get_template` to fetch the specific fields required for a document. Your agent then has the exact schema needed to construct a valid request.

Monitor signatures using LlamaIndex tools

Use `list_signed_agreements` to identify which documents are ready for the next stage of your process. The agent indexes these signed agreements, allowing you to query for completed deals by date or recipient. This creates a unified view of your signed documents. You can ask questions about your total active agreements without manually checking the dashboard.

Setup guide

Set up Common Paper 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 Common Paper 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 Common Paper tools.",
)
response = await agent.run("List recent Common Paper data")

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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 Common Paper. 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 Common Paper MCP in LlamaIndex

Use the BasicMCPClient to connect to the server and pass the tool spec to your FunctionAgent. The agent then ingests the API results into your vector store for semantic retrieval.

Yes. Once the data is indexed, you can perform natural language queries to find specific contracts or agreement statuses stored in your account.

You can use an allowed_tools filter to restrict which actions the agent performs. This keeps your RAG application focused on the specific contract data you need.

The data lives in your local vector store after the agent retrieves it. The MCP server acts only as the bridge to fetch that information.

Only the metadata you explicitly request is passed into your index. We avoid caching or persistent storage on our end, ensuring your signed agreements remain private and under your control.

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