DevSkiller 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 DevSkiller as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 DevSkiller. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in DevSkiller?"
)
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 DevSkiller MCP Server
Integrate DevSkiller, the technical screening and talent assessment platform, directly into your AI workflow. Manage your candidate pipeline, send test invitations, and retrieve detailed assessment reports and skill scores using natural language.
LlamaIndex agents combine DevSkiller 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
- Candidate Oversight — List and search for candidates in your database and monitor their current assessment status.
- Test Management — Access your library of available technical tests, coding tasks, and quizzes.
- Invitation Tracking — Monitor sent test invitations and track candidate progress in real-time.
- Performance Analytics — Retrieve full assessment reports with granular skill scores and performance metrics.
The DevSkiller 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 DevSkiller to LlamaIndex via MCP
Follow these steps to integrate the DevSkiller 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 DevSkiller
Why Use LlamaIndex with the DevSkiller MCP Server
LlamaIndex provides unique advantages when paired with DevSkiller through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DevSkiller tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DevSkiller tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DevSkiller, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DevSkiller tools were called, what data was returned, and how it influenced the final answer
DevSkiller + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DevSkiller MCP Server delivers measurable value.
Hybrid search: combine DevSkiller real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DevSkiller 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 DevSkiller for fresh data
Analytical workflows: chain DevSkiller queries with LlamaIndex's data connectors to build multi-source analytical reports
DevSkiller MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect DevSkiller to LlamaIndex via MCP:
get_account_metadata
Retrieve metadata and limits for your DevSkiller account
get_candidate_assessment_report
Retrieve the full assessment report for a candidate
get_candidate_profile
Get detailed information for a specific candidate
invite_candidate_to_test
Send a new test invitation to a candidate
list_assessment_candidates
List all candidates in your DevSkiller account
list_available_tests
List all assessment tests configured in your catalog
list_high_score_candidates
Identify candidates who achieved a score above a specific threshold
list_recently_sent_invitations
List test invitations sent in the last 24 hours
list_test_invitations
List all sent test invitations and their current status
search_candidates_by_identity
Search for a candidate by name or email keyword
Example Prompts for DevSkiller in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with DevSkiller immediately.
"List all candidates who scored above 85% in recent tests."
"Show me the assessment status for candidate 'john.doe@example.com'."
"Invite 'Sarah Smith' (sarah@example.com) to the 'Frontend React' test."
Troubleshooting DevSkiller MCP Server with LlamaIndex
Common issues when connecting DevSkiller to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDevSkiller + LlamaIndex FAQ
Common questions about integrating DevSkiller 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 DevSkiller 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 DevSkiller to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.