How to Use the Concord CLM MCP in LlamaIndex

Q: How do I ground RAG queries using Concord CLM data in LlamaIndex?

You use listagreements to pull contract data, which LlamaIndex then parses into nodes and indexes. Your query engine searches these nodes to answer questions with actual contract terms.

Q: Can LlamaIndex filter Concord CLM documents by status before indexing?

Yes, your agent can call listsignedagreements to retrieve only fully executed contracts. This ensures your vector store contains verified legal text instead of working drafts.

Q: How does the LlamaIndex agent handle template retrieval from Concord CLM?

The agent calls listtemplates to get available document structures. It then indexes these templates so users can query the exact layout requirements for new agreements.

Q: Can I search Concord CLM files by name using LlamaIndex tools?

Your agent uses searchagreementsbyname to find matching documents based on user queries. The tool returns specific agreement IDs that LlamaIndex then uses to fetch full details.

Index your Concord CLM contracts into LlamaIndex vector stores to ground your RAG queries in actual executed agreements.

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Connect Concord CLM MCP to LlamaIndex

Create your Vinkius account to connect Concord CLM 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 Concord CLM with LlamaIndex

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Index live agreements into LlamaIndex vector stores

The `list_agreements` tool fetches your active contract documents via the MCP Server so you can index them directly into your LlamaIndex knowledge base. This keeps your RAG applications grounded in actual executed contracts rather than outdated drafts. When users ask questions about active terms, the LlamaIndex query engine pulls fresh data retrieved by this tool. This eliminates hallucinations by referencing real, signed contract text.

Search contract metadata semantically

The `search_agreements_by_name` tool allows your agent to locate specific files before indexing their contents. Your query engine uses this to narrow down the search space to relevant documents only. Once located, `get_agreement` pulls the complete metadata schema into your index. This lets you filter search results by execution dates, signee names, or internal organization IDs.

Validate signature statuses during indexing

The `list_signed_agreements` tool filters your document repository to ensure only fully executed contracts enter your production index. You avoid indexing draft agreements that might contain unapproved clauses. If an index query reveals a missing signature, the agent uses `send_for_signature` to resolve the bottleneck. This keeps your document pipeline moving while maintaining a clean, verified knowledge base.

Setup guide

Set up Concord CLM 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 Concord CLM 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 Concord CLM tools.",
)
response = await agent.run("List recent Concord CLM 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 Concord. 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 Concord CLM MCP in LlamaIndex

You use `list_agreements` to pull contract data, which LlamaIndex then parses into nodes and indexes. Your query engine searches these nodes to answer questions with actual contract terms.

Yes, your agent can call `list_signed_agreements` to retrieve only fully executed contracts. This ensures your vector store contains verified legal text instead of working drafts.

The agent calls `list_templates` to get available document structures. It then indexes these templates so users can query the exact layout requirements for new agreements.

Your agent uses `search_agreements_by_name` to find matching documents based on user queries. The tool returns specific agreement IDs that LlamaIndex then uses to fetch full details.

All agreement texts and template schemas pass through an isolated Vinkius runtime. The MCP Server runs in an isolated Vinkius runtime, ensuring your proprietary contract clauses are never stored permanently outside Concord CLM.

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