How to Use the HotDocs MCP in LlamaIndex

Q: How do I index HotDocs generated PDFs using LlamaIndex?

Call getdocumentcontent through your tool spec and route the returned file buffer directly into a LlamaIndex document parser. The text is then embedded and stored in your vector database.

Q: Can I filter which HotDocs MCP Server tools LlamaIndex accesses?

You can restrict the tool list using the allowedtools parameter. This is useful if you only want an agent to read data via gettemplatepackage without permission to call updateanswers.

Index HotDocs templates and generated documents directly into LlamaIndex via this MCP Server. Build RAG applications over legal data.

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Connect HotDocs MCP to LlamaIndex

Create your Vinkius account to connect HotDocs 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 HotDocs with LlamaIndex

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Index HotDocs template metadata

This MCP Server connects LlamaIndex directly to your HotDocs Advance environment, turning document templates into a searchable knowledge base. Your pipeline calls `list_template_packages` and `list_template_versions` to ingest the structure of every available contract. When a user asks which form to use for a non-disclosure agreement, the query engine searches the indexed metadata. It finds the exact package ID and feeds it to `create_work_item` to begin the drafting process instantly.

Ground answers in assembled documents

LlamaIndex does not just trigger actions; it indexes the outputs. After calling `complete_assembly`, your agent uses `get_document_content` to download the finished PDF and immediately chunks it into your vector store. This creates a living repository of generated contracts. Users can query past agreements, and the system retrieves the exact clauses from documents fetched via `list_documents`, ensuring responses rely on real executed files instead of base models.

Connect the MCP Server to audit logs

You can build RAG pipelines that understand your document generation volume. A data agent queries `list_work_items_by_date` and stores the resulting JSON records into an index. Managers can then ask natural language questions about workflow bottlenecks. The query engine reads the indexed states from `get_work_item` and reports exactly how many templates stalled waiting for user input.

Setup guide

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

Call `get_document_content` through your tool spec and route the returned file buffer directly into a LlamaIndex document parser. The text is then embedded and stored in your vector database.

Yes. You can configure a data ingestion pipeline that calls `get_unanswered_variables` and indexes the results, allowing users to query which contracts are missing specific client details.

Initialize the BasicMCPClient with the server URL, wrap it in an McpToolSpec, and pass the resulting tools to your agent. The agent handles the parameter mapping for endpoints like `create_interview_session`.

You can restrict the tool list using the allowed_tools parameter. This is useful if you only want an agent to read data via `get_template_package` without permission to call `update_answers`.

This integration handles sensitive contract PDFs and template variables. Your vector store and the server run in your controlled environment, ensuring proprietary legal language remains isolated within your infrastructure boundaries.

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