How to Use the GlassFrog MCP in LlamaIndex

Q: How does LlamaIndex prevent hallucinations when reading GlassFrog roles?

We ground every response in the actual data retrieved from listholacracyroles. This means the agent only answers using the exact text stored in your vector index.

Index your GlassFrog data into LlamaIndex vector stores using this MCP Server for accurate, zero-hallucination queries.

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Connect GlassFrog MCP to LlamaIndex

Create your Vinkius account to connect GlassFrog 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 GlassFrog with LlamaIndex

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Index active policies for semantic search

The `list_circle_policies` tool extracts all written rules and constraints established across your organization's circles. LlamaIndex takes this raw text and builds a searchable vector index so your agent can retrieve exact rules instantly. Instead of guessing what is allowed, your query engine references these indexed policies directly. This process eliminates hallucinations by grounding the agent's answers in your verified Holacracy records.

Query project history using an MCP Server index

The `list_tactical_projects` tool pulls the current list of active initiatives and their owners from your workspace. Your application indexes this data to let team members query project status using natural language. When someone asks what a specific team is working on, the index retrieves the relevant project records. This method replaces manual dashboard searching with a simple, direct conversational interface.

Connect role definitions to your knowledge base

The `list_holacracy_roles` tool fetches every defined role, purpose, and accountabilities from your organization. LlamaIndex embeds these definitions, creating a semantic map of who is responsible for what. Your agent uses this map to route tasks to the correct role filler based on their documented accountabilities. You no longer have to manually search through organizational charts to find the right owner.

Setup guide

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

Start by initializing the MCP tool spec. Next, pass the output of tools like `list_circle_policies` directly to your index writer to build your knowledge base.

Yes, you can configure your pipeline to pull fresh data from the server periodically. Doing this keeps your vector index synchronized with the latest circle and role changes.

You can restrict the agent's scope using the allowed tools filter during initialization. Such limits prevent the agent from calling mutation tools when you only want it to read data.

We ground every response in the actual data retrieved from `list_holacracy_roles`. This means the agent only answers using the exact text stored in your vector index.

All project details and member emails retrieved during indexing are processed in memory within your secure environment. To ensure your operational data is never cached or leaked, Vinkius uses ephemeral sandbox execution.

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