Gem MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Gem 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 Gem. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Gem?"
)
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 Gem MCP Server
Connect your Gem recruitment CRM to any AI agent to automate your talent sourcing and candidate relationship management through the Model Context Protocol (MCP). Gem is the AI-first recruiting platform that helps modern teams find, engage, and hire top talent. This MCP server enables you to manage your candidate database, organize talent projects, and retrieve detailed interaction histories directly through natural conversation.
LlamaIndex agents combine Gem tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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.
Key Features
- Candidate Oversight — List all candidates in your CRM, retrieve detailed profiles, and create new records instantly to maintain a full talent pipeline.
- Talent Project Management — Access your sourcing projects and fetch detailed metadata to track how your talent pools are growing.
- Interaction Logging — Access historical notes and interaction logs for candidates, or programmatically log new notes from your chat interface.
- Outreach Insights — List active outreach sequences and automated messaging campaigns to understand your engagement status.
- Custom Metadata — Retrieve the definitions of custom fields configured for your team to maintain consistent data tracking.
- Workforce Collaboration — List all team members and recruiters in the account to verify ownership and assignments.
- Real-time Synchronization — Keep your recruiting data accessible to your AI assistant without leaving your primary workspace.
The Gem MCP Server exposes 12 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 Gem to LlamaIndex via MCP
Follow these steps to integrate the Gem 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 12 tools from Gem
Why Use LlamaIndex with the Gem MCP Server
LlamaIndex provides unique advantages when paired with Gem through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Gem tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Gem tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Gem, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Gem tools were called, what data was returned, and how it influenced the final answer
Gem + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Gem MCP Server delivers measurable value.
Hybrid search: combine Gem real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Gem 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 Gem for fresh data
Analytical workflows: chain Gem queries with LlamaIndex's data connectors to build multi-source analytical reports
Gem MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Gem to LlamaIndex via MCP:
add_candidate_note
Log an interaction
create_crm_candidate
Add new candidate
get_candidate_details
Get candidate metadata
get_project_details
Get project metadata
list_candidate_notes
List interactions
list_candidates
List CRM candidates
list_crm_custom_fields
List team fields
list_outreach_sequences
List sequences
list_recruiting_team
List Gem users
list_talent_projects
List Gem projects
update_crm_candidate
Modify candidate
verify_api_connection
Check connection
Example Prompts for Gem in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Gem immediately.
"List all active candidates in my Gem CRM."
"Show me the interactions history for candidate '12345'."
"Create a new candidate record for 'Alice Smith' (alice@email.com)."
Troubleshooting Gem MCP Server with LlamaIndex
Common issues when connecting Gem to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGem + LlamaIndex FAQ
Common questions about integrating Gem 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 Gem with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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 Gem to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.