GlassFrog MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add GlassFrog 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 GlassFrog. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in GlassFrog?"
)
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 GlassFrog MCP Server
Connect your GlassFrog organization to any AI agent to automate your Holacracy governance and tactical operations through the Model Context Protocol (MCP). GlassFrog is the premier platform for self-management and organizational clarity. This MCP server enables you to retrieve circle structures, role definitions, project lists, and performance metrics directly through natural conversation.
LlamaIndex agents combine GlassFrog tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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.
Key Features
- Organizational Oversight — List all circles and role definitions, retrieving detailed purposes and accountabilities for every role in the organization.
- Tactical Project Management — Access and list tactical projects, and programmatically create new projects within specific circles from your chat interface.
- Governance Transparency — Retrieve circle policies and governance records to understand the rules and constraints of your organization.
- Performance Metrics — Access defined metrics and checklist items to track organizational health and tactical progress.
- Workforce Collaboration — List all organization members and search for specific people by email to verify role assignments.
- Real-time Synchronization — Keep your Holacracy data accessible to your AI assistant without leaving your primary workspace.
The GlassFrog MCP Server exposes 12 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.
How to Connect GlassFrog to LlamaIndex via MCP
Follow these steps to integrate the GlassFrog MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from GlassFrog
Why Use LlamaIndex with the GlassFrog MCP Server
LlamaIndex provides unique advantages when paired with GlassFrog through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine GlassFrog tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain GlassFrog tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query GlassFrog, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what GlassFrog tools were called, what data was returned, and how it influenced the final answer
GlassFrog + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the GlassFrog MCP Server delivers measurable value.
Hybrid search: combine GlassFrog real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query GlassFrog 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 GlassFrog for fresh data
Analytical workflows: chain GlassFrog queries with LlamaIndex's data connectors to build multi-source analytical reports
GlassFrog MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect GlassFrog to LlamaIndex via MCP:
create_new_project
Add a project
find_member_by_email
Search member
get_circle_summary
Get circle overview
list_checklist_items
List checklists
list_circle_metrics
List metrics
list_circle_policies
List policies
list_holacracy_circles
List all circles
list_holacracy_roles
List role definitions
list_org_members
List people
list_role_assignments
List assignments
list_tactical_projects
List all projects
verify_api_connection
Check connection
Example Prompts for GlassFrog in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with GlassFrog immediately.
"List all active circles in my organization."
"What are the accountabilities for the 'Product Manager' role?"
"Show me the current tactical metrics for the 'Marketing' circle (ID: 123)."
Troubleshooting GlassFrog MCP Server with LlamaIndex
Common issues when connecting GlassFrog to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGlassFrog + LlamaIndex FAQ
Common questions about integrating GlassFrog MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect GlassFrog with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect GlassFrog to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.