How to Use the GatherUp MCP in LlamaIndex

Q: Can I filter which GatherUp tools LlamaIndex sees?

Absolutely. Use the allowedtools filter during the MCP client setup to restrict access to specific operations like getaccountinfo.

Q: Can LlamaIndex use GatherUp to search across multiple locations?

You iterate through listbusinesslocations to gather IDs. Then, you query each location using searchallreviews to aggregate the full data set.

Index your customer feedback into LlamaIndex using the GatherUp MCP server for grounded, knowledge-based responses.

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Connect GatherUp MCP to LlamaIndex

Create your Vinkius account to connect GatherUp 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 GatherUp with LlamaIndex

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Build a searchable knowledge base for LlamaIndex

Use `list_online_reviews` to pull raw text into your vector store. LlamaIndex then indexes this data so your agents query actual past feedback instead of guessing. This turns your static reviews into a dynamic index. When you ask about customer sentiment, the agent pulls verified historical data through the MCP connection.

Query metrics directly in LlamaIndex

Call `get_review_metrics` to retrieve live performance data for your business locations. The agent adds these numbers to its context window for accurate reporting. It allows for complex reasoning over quantitative data. You get answers based on current status, not stale reports.

Manage internal feedback via MCP tools

Expose `list_internal_feedback` to your agent for semantic search within LlamaIndex. It helps the system connect internal notes to external review trends. Use `reply_to_feedback` to act on these insights immediately. The agent maintains the thread, ensuring all feedback is addressed and cataloged correctly.

Setup guide

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

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Common questions about GatherUp MCP in LlamaIndex

The client fetches data via the tool list and processes the output into your vector store. It treats API results as documents for semantic indexing.

Absolutely. Use the allowed_tools filter during the MCP client setup to restrict access to specific operations like `get_account_info`.

Yes, you can manage state by maintaining your connection instance. This ensures the agent remembers previous tool calls during a long-running RAG process.

Your sensitive review text and customer contact information is processed through an encrypted, ephemeral sandbox. No data persists outside your local environment.

You iterate through `list_business_locations` to gather IDs. Then, you query each location using `search_all_reviews` to aggregate the full data set.

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