How to Use the Affinity MCP in LlamaIndex

Q: Can I filter Affinity results in LlamaIndex?

Yes, you can use the allowedtools filter to restrict which CRM data the agent can access. This keeps your search index focused on relevant records.

Index your relationship data into LlamaIndex for grounded, semantic search.

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Connect Affinity MCP to LlamaIndex

Create your Vinkius account to connect Affinity 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 Affinity with LlamaIndex

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Index Affinity interactions in LlamaIndex

Use `list_interactions` to extract your team's communication history and push it into a vector store. This turns your raw CRM logs into a searchable knowledge base. Your RAG application can now answer questions about past meetings without guessing. The index grounds every response in actual history.

Searchable Affinity lists for LlamaIndex apps

Retrieve your custom pipelines using `list_smart_lists` and index them for quick lookup. You can then use `list_list_entries` to find specific stakeholders within those lists. It allows your agent to query the CRM as if it were a local document. You are building systems where API data and knowledge bases exist as one.

Map company data inside LlamaIndex

Pull your organization list via `list_organizations` to enrich your semantic search results. Your LlamaIndex agent can now cross-reference contacts with their mapped companies. This creates a unified view of your network. The agent retrieves the data and indexes it for future queries.

Setup guide

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

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Common questions about Affinity MCP in LlamaIndex

Use the BasicMCPClient to connect and load the tools into a McpToolSpec. From there, you can index the output and pass it to your FunctionAgent.

Because you are indexing actual CRM data, the agent bases its answers on the retrieved logs. This removes the reliance on internal model training data.

Your CRM data is pulled only when your agent triggers a query. We use zero-trust transport and do not persist your interaction logs on our side.

Yes, you can use the allowed_tools filter to restrict which CRM data the agent can access. This keeps your search index focused on relevant records.

The server retrieves interaction logs, person records, and organization mappings. These are treated as raw text objects for your vector store.

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