How to Use the Sproof MCP in LlamaIndex

Q: How does LlamaIndex use Sproof data?

It takes the structured output from tools like listaudittrail and transforms it into embeddings. This allows you to query based on meaning, not just exact keywords.

Q: Can I search past signature requests using LlamaIndex?

Absolutely. By indexing the results of getsignaturerequest, you can build a system where users ask questions about old signings and get answers backed by Sproof's live data.

Q: How do I verify data privacy with Sproof and LlamaIndex?

The server touches standard user profile data (getprofile) and structured documents. When building your index, you must ensure the underlying source data adheres to required compliance standards.

Q: What is the best way to query Sproof using LlamaIndex?

Always start by retrieving list views, like listdocuments or listsignaturerequests. Then index those results so your users can ask complex questions against them.

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Connect Sproof MCP to LlamaIndex

Create your Vinkius account to connect Sproof 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 Sproof with LlamaIndex

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Indexing Signature Requests with LlamaIndex

When you run `list_signature_requests`, the output feeds directly into your vector store. This means you can query past signing sessions and get answers grounded in actual API data, not just general knowledge. The results from this tool are indexed alongside your documents, letting users search across both unstructured text and structured signature metadata.

Searchable Document Metadata with LlamaIndex MCP Server

Use `list_documents` to pull document details. By indexing these results, you can ask natural language questions like, 'Which documents are associated with Client X?' and get a precise answer from the knowledge base. The index combines live data retrieval with semantic search, making your application much more useful than simple API calls.

Tracking Contacts for LlamaIndex

`list_contacts` provides a roster of all current contacts. Indexing this list lets you build an internal knowledge graph that answers questions like, 'Who is the primary contact for department Y?' without needing to write complex SQL queries. This capability turns raw API lists into searchable, context-aware knowledge.

Setup guide

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

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Common questions about Sproof MCP in LlamaIndex

It takes the structured output from tools like `list_audit_trail` and transforms it into embeddings. This allows you to query based on meaning, not just exact keywords.

Absolutely. By indexing the results of `get_signature_request`, you can build a system where users ask questions about old signings and get answers backed by Sproof's live data.

It indexes structured JSON objects returned from the MCP tools. This includes document details, contact lists, and signature statuses, turning them into searchable knowledge chunks.

The server touches standard user profile data (`get_profile`) and structured documents. When building your index, you must ensure the underlying source data adheres to required compliance standards.

Always start by retrieving list views, like `list_documents` or `list_signature_requests`. Then index those results so your users can ask complex questions against them.

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