How to Use the Clustdoc MCP in LlamaIndex

Q: How do I index Clustdoc application data inside LlamaIndex?

You use BasicMCPClient to connect the server, then fetch files with listapplications to load them as documents. LlamaIndex indexes these records into your vector store for semantic querying.

Q: Can LlamaIndex agents trigger a Clustdoc application creation?

Yes, the agent uses createapplication when its query routing decides a new onboarding file is needed. It formats the required JSON payload using context extracted from your index.

Q: How does this Clustdoc integration prevent hallucinated workflows in LlamaIndex?

By fetching live templates with listworkflows, the agent only references active onboarding processes. It queries real-time API schemas instead of guessing the required documents.

Q: Can I filter which tools this MCP server can access in my framework?

Yes, you use the allowedtools filter during your tool spec initialization. This lets you restrict your agent to read-only tools like getapplicationdetails while blocking write actions.

Index Clustdoc onboarding statuses into LlamaIndex vector stores to query active applications with natural language.

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Connect Clustdoc MCP to LlamaIndex

Create your Vinkius account to connect Clustdoc 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 Clustdoc with LlamaIndex

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Index application data using this MCP Server

The `list_applications` tool pulls your entire client roster and feeds it directly into your LlamaIndex document store via our MCP adapter. This converts raw onboarding metadata into searchable text nodes for your vector database. Your agent queries this index to find patterns in client delays without hitting the live API repeatedly. You get instant answers about who hasn't submitted their paperwork yet.

Query onboarding paths in LlamaIndex

The `list_workflows` tool exposes your structured intake templates to your LlamaIndex RAG pipeline. This lets your agent recommend the exact onboarding sequence based on past successful runs. By grounding your agent in actual workflow data, you prevent it from hallucinating non-existent steps during client setups. It checks the live schema before suggesting any action.

Track team assignments with semantic search

The `list_team_members` tool extracts your staff list and maps it against active client files retrieved by `get_application_details`. This builds an in-memory graph of who is handling which onboarding accounts. When a client asks for help, LlamaIndex searches this local index to find the assigned representative instantly. It removes the need to manually browse the web dashboard.

Setup guide

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

You use `BasicMCPClient` to connect the server, then fetch files with `list_applications` to load them as documents. LlamaIndex indexes these records into your vector store for semantic querying.

Yes, the agent uses `create_application` when its query routing decides a new onboarding file is needed. It formats the required JSON payload using context extracted from your index.

By fetching live templates with `list_workflows`, the agent only references active onboarding processes. It queries real-time API schemas instead of guessing the required documents.

Yes, you use the `allowed_tools` filter during your tool spec initialization. This lets you restrict your agent to read-only tools like `get_application_details` while blocking write actions.

This MCP integration processes your sensitive webhook configs and onboarding workflows in an ephemeral V8 sandbox. No database configurations or team lists are ever stored on Vinkius servers.

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