Dovetail MCP Server for LlamaIndexGive LlamaIndex instant access to 7 tools to Create Insight, Create Note, Get Project Details, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Dovetail as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Dovetail app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 7 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Dovetail. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Dovetail?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Dovetail MCP Server
Connect your Dovetail account to any AI agent and take full control of your user research and insight management workflows through natural conversation.
LlamaIndex agents combine Dovetail tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Project Orchestration — List and manage research projects programmatically and retrieve detailed metadata about goals and participants
- Note Architecture — Create and organize research notes (interviews, usability tests, raw data) with specific content types (HTML, Markdown) directly from your agent
- Insight Management — Programmatically publish research findings and summaries to maintain a high-fidelity record of your team's discoveries
- Deep Search — Find relevant research data across projects using powerful query filters for titles and content
- Workspace Visibility — Retrieve complete directories of workspace members to coordinate collaboration and manage team access
The Dovetail MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 7 Dovetail tools available for LlamaIndex
When LlamaIndex connects to Dovetail through Vinkius, your AI agent gets direct access to every tool listed below — spanning dovetail, user-research, insights-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new research insight
Create a new research note
Get details for a research project
List research insights
List research notes
List all research projects
List workspace members
Connect Dovetail to LlamaIndex via MCP
Follow these steps to wire Dovetail into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Dovetail MCP Server
LlamaIndex provides unique advantages when paired with Dovetail through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Dovetail tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Dovetail tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Dovetail, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Dovetail tools were called, what data was returned, and how it influenced the final answer
Dovetail + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Dovetail MCP Server delivers measurable value.
Hybrid search: combine Dovetail real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Dovetail to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Dovetail for fresh data
Analytical workflows: chain Dovetail queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Dovetail in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Dovetail immediately.
"List all my research projects in Dovetail."
"Create a new research note 'User A Interview' in project 'proj_123'."
"Show me all published insights containing the word 'mobile'."
Troubleshooting Dovetail MCP Server with LlamaIndex
Common issues when connecting Dovetail to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDovetail + LlamaIndex FAQ
Common questions about integrating Dovetail MCP Server with LlamaIndex.