Credly MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Credly 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 Credly. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Credly?"
)
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 Credly MCP Server
Integrate Credly, the world's largest digital credential network, directly into your AI workflow. Manage your organization's badge templates, audit issued credentials, and track member skills using natural language.
LlamaIndex agents combine Credly tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.
What you can do
- Badge Management — List all issued badges and explore your organization's badge templates.
- Issuance Auditing — Monitor which recipients have received badges and track authorized issuers.
- Skill Tracking — Explore the full inventory of skills mapped to your digital credentials.
- Organization Insights — Retrieve metadata and member lists for your connected organizations.
The Credly MCP Server exposes 10 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 Credly to LlamaIndex via MCP
Follow these steps to integrate the Credly 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 10 tools from Credly
Why Use LlamaIndex with the Credly MCP Server
LlamaIndex provides unique advantages when paired with Credly through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Credly tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Credly tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Credly, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Credly tools were called, what data was returned, and how it influenced the final answer
Credly + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Credly MCP Server delivers measurable value.
Hybrid search: combine Credly real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Credly 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 Credly for fresh data
Analytical workflows: chain Credly queries with LlamaIndex's data connectors to build multi-source analytical reports
Credly MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Credly to LlamaIndex via MCP:
get_badge_details
Get detailed information for a specific issued badge
get_organization_info
Get metadata for a specific organization
get_template_details
Get full design and criteria for a badge template
list_authorized_issuers
List people authorized to issue badges
list_badge_recipients
List people who have received badges
list_badge_skills
List all skills mapped to badges in the system
list_badge_templates
List all badge templates available for issuance
list_connected_organizations
List organizations connected to your account
list_issued_badges
List all badges issued by your organization
list_org_members
List all members of your organization on Credly
Example Prompts for Credly in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Credly immediately.
"List all badges issued by my organization in the last month."
"Show me the details for badge ID 'b8s9df7'."
"What skills are associated with the 'Senior DevOps Engineer' badge template?"
Troubleshooting Credly MCP Server with LlamaIndex
Common issues when connecting Credly to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCredly + LlamaIndex FAQ
Common questions about integrating Credly 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 Credly 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 Credly to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.