How to Use the Sally MCP in LlamaIndex

Q: Can I filter which Sally tools LlamaIndex sees?

Yes. You can use the allowedtools filter to limit the agent to specific functions like gettask.

Index your frontline data with LlamaIndex. Turn Sally task logs into a searchable knowledge base for your RAG applications.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Sally MCP to LlamaIndex

Create your Vinkius account to connect Sally to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Sally with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Semantic search for Sally tasks

LlamaIndex pulls data from `list_tasks` and indexes it into your vector store. Your agent no longer guesses; it queries the index for exact project details. This turns your static task list into a dynamic knowledge base. You can ask questions about past project history and get grounded answers.

Ground your agent in Sally data

Use the MCP tool spec to combine live `get_project` data with your existing documents. The agent retrieves context from the API and your local files simultaneously. This prevents hallucinations by forcing the agent to rely on actual workspace records. It provides the accuracy needed for operational decision making.

Query frontline boards via LlamaIndex

The `get_board` tool output is ingested directly into your index. Your RAG app can now interpret Kanban board states as part of its reasoning process. It maps real-world status to your search queries. The agent understands where tasks sit without needing a manual refresh of the board.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Sally. 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.