How to Use the GlassFrog MCP in LangChain

Run multi-step governance workflows by chaining GlassFrog operations directly inside your LangChain MCP agent.

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Connect GlassFrog MCP to LangChain

Create your Vinkius account to connect GlassFrog to LangChain 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 LangChain

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Map circle structures with LangChain pipelines

The `list_holacracy_circles` tool pulls the complete layout of your active organization circles directly into your runtime context. Your agent parses this structural data to identify parent-child relationships before feeding the circle IDs into subsequent chain steps. By feeding this output into `get_circle_summary`, your pipeline automatically extracts operational health metrics without manual scripting. LangSmith tracks each transition, showing you exactly how the agent navigated from the broad organization map down to specific circle details.

Check metrics and checklists before meetings

The `list_circle_metrics` tool retrieves current key performance indicators for any designated circle in your workspace. LangChain agents can immediately pass these numbers to a processing node that compares current performance against historical targets. Right after that, the agent calls `list_checklist_items` to pull recurring operational tasks that require status updates. Combining these two datasets gives your team an automated, objective summary of circle health before your tactical meeting even starts.

Verify governance compliance in real-time

The `list_role_assignments` tool exposes who holds which accountability within your organization structure. Your active agent runs this check to verify that critical roles are not left vacant or double-allocated. If the agent detects a gap, it calls `find_member_by_email` to find the correct contact info and flag the missing assignment. This automated audit loop ensures your actual operations match your documented Holacracy constitution.

Setup guide

Set up GlassFrog MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes GlassFrog tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "glassfrog-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent GlassFrog transactions"
    })
    print(result["messages"][-1].content)

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Common questions about GlassFrog MCP in LangChain

First, install the adapter package to connect the server. After that, pass the tools retrieved from `client.get_tools()` straight to your agent executor.

Yes, LangSmith captures every single tool call made by the agent. Check the run history to see the exact inputs and outputs of operations like `list_tactical_projects`.

Your agent decides which endpoint to call based on the output of the previous step. In practice, it can search for a user, find their circle, and then list their specific role definitions in a single run.

Yes, you can. LangChain allows you to combine these governance tools with any of its 500+ standard integrations in the same execution graph.

Your organizational structures, role definitions, and meeting metrics stay within your local runtime environment. Because Vinkius runs the MCP server in an isolated sandbox, your raw governance data is never stored or used to train public models.

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