Cleared (ClearedIn) MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cleared (ClearedIn) 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 Cleared (ClearedIn). "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Cleared (ClearedIn)?"
)
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 Cleared (ClearedIn) MCP Server
Connect your Cleared (ClearedIn) account to any AI agent and take full control of your identity verification and screening workflows through natural conversation. Streamline how you verify users and maintain trust natively.
LlamaIndex agents combine Cleared (ClearedIn) 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
- Verification Oversight — List and retrieve details for all identity verification requests and their status natively
- Screening Intelligence — Access and monitor background screening requests and retrieve detailed results flawlessly
- Digital Signatures — List all digital signature requests and track their completion status securely
- Audit Logistics — Access security audit logs to track who accessed sensitive data and when flawlessly
- Identity Management — Retrieve core account and user metadata directly within your workspace flawlessly
- Trust Logistics — Monitor the lifecycle of verification sessions from 'Pending' to 'Verified' or 'Rejected' in real-time
The Cleared (ClearedIn) 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 Cleared (ClearedIn) to LlamaIndex via MCP
Follow these steps to integrate the Cleared (ClearedIn) 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 Cleared (ClearedIn)
Why Use LlamaIndex with the Cleared (ClearedIn) MCP Server
LlamaIndex provides unique advantages when paired with Cleared (ClearedIn) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cleared (ClearedIn) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cleared (ClearedIn) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cleared (ClearedIn), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cleared (ClearedIn) tools were called, what data was returned, and how it influenced the final answer
Cleared (ClearedIn) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cleared (ClearedIn) MCP Server delivers measurable value.
Hybrid search: combine Cleared (ClearedIn) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cleared (ClearedIn) 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 Cleared (ClearedIn) for fresh data
Analytical workflows: chain Cleared (ClearedIn) queries with LlamaIndex's data connectors to build multi-source analytical reports
Cleared (ClearedIn) MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Cleared (ClearedIn) to LlamaIndex via MCP:
get_cleared_account_info
Retrieve core account and user metadata
get_screening_details
Get detailed results for a specific screening
get_signature_details
Get detailed status for a specific signature request
get_verification_details
Get detailed information for a specific verification
list_background_screenings
List all background screening requests
list_cleared_audit_logs
List security audit logs for the account
list_digital_signatures
List all digital signature requests
list_identity_verifications
List all identity verification requests
Example Prompts for Cleared (ClearedIn) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Cleared (ClearedIn) immediately.
"List the last 5 identity verifications in Cleared."
"Show me the results for screening ID 'scr_12345'."
"What is the status of the signature request for 'Employment Contract'?"
Troubleshooting Cleared (ClearedIn) MCP Server with LlamaIndex
Common issues when connecting Cleared (ClearedIn) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCleared (ClearedIn) + LlamaIndex FAQ
Common questions about integrating Cleared (ClearedIn) 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 Cleared (ClearedIn) 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 Cleared (ClearedIn) to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.