Can I see the code a candidate wrote in a pad through the agent?

Yes! Use the `get_pad_session_details` tool with the pad ID. The agent will return the current `contents` of the pad, allowing you to review the code without opening your browser.

How do I check the full event log for an interview session?

Use the `get_pad_event_log` tool. Your agent will fetch a play-by-play history of all actions in the pad, which is useful for auditing the candidate's process and execution steps.

Where do I find my CoderPad API Key?

Log in to CoderPad, navigate to **Settings**, and select **API**. You can generate and copy your API key from that section.

CoderPad MCP Server for LlamaIndex 8 tools — connect in under 2 minutes

Name: CoderPad
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CoderPad as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 CoderPad. "
            "You have 8 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in CoderPad?"
    )
    print(response)

asyncio.run(main())

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About CoderPad MCP Server

Connect your CoderPad account to any AI agent and take full control of your technical hiring process through natural conversation. Streamline how you prepare, conduct, and review technical interviews natively.

LlamaIndex agents combine CoderPad tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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

Pad Management — Create and list live collaborative coding pads for technical interviews natively
Session Intelligence — Access detailed information for specific pads, including the current code contents and status flawlessly
Event Tracking — Retrieve a play-by-play log of all actions within an interview session, including typing and execution flawlessly
Question Logistics — List and review available interview questions from your organization's question bank securely
Team Management — List all users and interviewers within your organization to manage access flawlessly
integrated Visibility — Retrieve detailed pad metadata including titles, languages, and candidate names directly within your workspace

The CoderPad MCP Server exposes 8 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 CoderPad to LlamaIndex via MCP

Follow these steps to integrate the CoderPad 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 8 tools from CoderPad

Why Use LlamaIndex with the CoderPad MCP Server

LlamaIndex provides unique advantages when paired with CoderPad through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine CoderPad tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain CoderPad tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query CoderPad, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what CoderPad tools were called, what data was returned, and how it influenced the final answer

CoderPad + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the CoderPad MCP Server delivers measurable value.

Hybrid search: combine CoderPad real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query CoderPad 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 CoderPad for fresh data

Analytical workflows: chain CoderPad queries with LlamaIndex's data connectors to build multi-source analytical reports

CoderPad MCP Tools for LlamaIndex (8)

These 8 tools become available when you connect CoderPad to LlamaIndex via MCP:

create_new_interview_pad

Create a new live collaborative coding pad

get_coderpad_usage_history

Retrieve a history of pad usage and quota consumption

get_my_coderpad_profile

Retrieve information about the authenticated user

get_pad_event_log

Retrieve a play-by-play log of all actions in a specific pad

get_pad_session_details

Get detailed information for a specific pad, including current code contents

list_coderpad_org_users

List all users and interviewers in the organization account

list_coderpad_questions

List available interview questions from the question bank

list_coderpad_sessions

List all technical interview pads (sessions)

Example Prompts for CoderPad in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with CoderPad immediately.

"List all my CoderPad sessions from this week."

"Create a new Python pad for 'Junior Engineer Interview'."

"Show me the last 5 questions in my question bank."

Troubleshooting CoderPad MCP Server with LlamaIndex

Common issues when connecting CoderPad to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

CoderPad + LlamaIndex FAQ

Common questions about integrating CoderPad MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query CoderPad tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.