How to Use the DottedSign MCP in LlamaIndex

Q: How does LlamaIndex index DottedSign task data?

The LlamaIndex MCP tool spec fetches data using tools like listtasks. LlamaIndex then parses these JSON payloads into document objects, which are vectorized and stored in your index for semantic retrieval.

Q: Can I query my DottedSign templates using LlamaIndex semantic search?

Yes, you can. The framework calls listtemplates to gather your template metadata, indexes the text, and allows your agent to find the right template ID using natural language queries.

Q: How do I run a grounded Q&A session over my active DottedSign tasks in LlamaIndex?

You load the tools into a FunctionAgent. The agent uses retrievetask to pull live document details, ensuring its answers about signer progress are grounded in real-time API data.

Q: Can LlamaIndex clean up canceled tasks from its index?

When your agent runs canceltask or deletetask, you must trigger a re-indexing step. This keeps your vector store synchronized with the actual state of your document queue.

Index your DottedSign document metadata into LlamaIndex vector stores for semantic search and grounded RAG queries.

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Connect DottedSign MCP to LlamaIndex

Create your Vinkius account to connect DottedSign 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 DottedSign with LlamaIndex

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Ground your LlamaIndex RAG in live signing data

This MCP Server provides live signing data that LlamaIndex indexes for grounded RAG applications. By pulling real-time document states via `list_tasks`, the framework converts active agreements into searchable index nodes. Your queries about pending agreements will be answered with exact, live API data instead of static guesses. The framework indexes the output of `retrieve_task` alongside your vector store documents. When a user asks who needs to sign next, the system retrieves the actual task state rather than hallucinated details.

Build search indexes of your templates

The DottedSign MCP Server exposes your templates so LlamaIndex can build semantic search indexes. By exposing `list_templates` to your setup, the framework indexes template descriptions and metadata. This lets your query engine find the exact template needed for a specific client profile. Once the correct template is located semantically, the `create_task_from_template` tool initiates the signature process. You get a direct bridge from natural language search to active contract execution.

Index audit trails for compliance search

This MCP Server enables LlamaIndex to retrieve and index audit trails for compliance search. Your query engine can call `get_audit_trail` and index the execution history of every document. This setup allows you to run semantic queries to find when a specific IP address signed a contract. If a task is disputed, the system pulls the exact history using `get_audit_trail`. This keeps your compliance verification fast, accurate, and completely automated within your existing knowledge base.

Setup guide

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

The LlamaIndex MCP tool spec fetches data using tools like `list_tasks`. LlamaIndex then parses these JSON payloads into document objects, which are vectorized and stored in your index for semantic retrieval.

Yes, you can. The framework calls `list_templates` to gather your template metadata, indexes the text, and allows your agent to find the right template ID using natural language queries.

You load the tools into a FunctionAgent. The agent uses `retrieve_task` to pull live document details, ensuring its answers about signer progress are grounded in real-time API data.

When your agent runs `cancel_task` or `delete_task`, you must trigger a re-indexing step. This keeps your vector store synchronized with the actual state of your document queue.

Vinkius runs the server in an isolated, ephemeral V8 sandbox with zero-trust architecture. Your raw task details, signer emails, and audit trails are never cached or used for training, keeping your legal data private.

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