How to Use the Credly MCP in LlamaIndex

Q: How can LlamaIndex search my Credly badges by skill?

You'd build an agent that calls listbadgeskills and listbadgetemplates, then ingests that output into a LlamaIndex vector store. This creates a searchable index where you can find badge templates based on the skills they represent. It connects two different types of Credly data into one searchable source.

Q: Can I build a LlamaIndex query engine over my Credly organization's members?

Yes. You can set up a recurring job that runs listorgmembers and listauthorizedissuers, then has LlamaIndex index the results. This gives you a queryable snapshot of your organization's structure and permissions on Credly.

Q: What's the setup to connect LlamaIndex to Credly?

It's simple. Use the McpToolSpec from the official LlamaIndex integration. You just point it at your Vinkius server URL and it generates the tool definitions for your agent automatically.

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Connect Credly MCP to LlamaIndex

Create your Vinkius account to connect Credly 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 Credly with LlamaIndex

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Index Your Badge Ecosystem

Use the Credly tools to pull data, and let LlamaIndex automatically index it into a vector store. Run a process that calls `list_badge_templates` and `get_template_details` for every badge in your organization. The output is a searchable index of your entire badge catalog. Now you can ask plain English questions like, "which badges do we have for project management?" Your LlamaIndex app will give you answers grounded in your actual Credly data, not a hallucination.

Query Your Issuance History

This is more than just getting a list. Use tools like `list_issued_badges` and `list_badge_recipients` to systematically pull your history, then have LlamaIndex ingest it. This creates a knowledge base you can query to understand past activity. Your RAG application can now answer complex questions like, "Show me all the designers who earned a UX badge last quarter." LlamaIndex synthesizes the answer from the indexed tool outputs, turning historical data into a usable resource.

The Credly MCP Server for RAG

Connect LlamaIndex to the Credly MCP server to build RAG applications with live, real-time data. Instead of only using static documents, your agent can use `get_organization_info` or `list_org_members` to fetch the absolute latest information from the Credly platform. This turns your agent from a simple Q&A bot into a live reporting tool. It can combine data from a static policy document with an up-to-the-minute list of badge recipients from Credly, giving you a complete and current picture.

Setup guide

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

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Common questions about Credly MCP in LlamaIndex

You'd build an agent that calls `list_badge_skills` and `list_badge_templates`, then ingests that output into a LlamaIndex vector store. This creates a searchable index where you can find badge templates based on the skills they represent. It connects two different types of Credly data into one searchable source.

The server itself stores nothing. Your LlamaIndex application is what decides to persist the tool outputs into a vector store for RAG. The MCP server is just the secure, managed pipe for getting the live Credly data to your application.

Yes. You can set up a recurring job that runs `list_org_members` and `list_authorized_issuers`, then has LlamaIndex index the results. This gives you a queryable snapshot of your organization's structure and permissions on Credly.

It's simple. Use the `McpToolSpec` from the official LlamaIndex integration. You just point it at your Vinkius server URL and it generates the tool definitions for your agent automatically.

The server is a secure proxy. It uses your token to make live API calls to Credly and immediately returns the data, such as badge template designs or recipient lists. Nothing is logged or stored on the Vinkius platform; every transaction is isolated and ephemeral.

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