TrueFoundry 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 TrueFoundry 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 TrueFoundry. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in TrueFoundry?"
)
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 TrueFoundry MCP Server
What you can do
Connect AI agents to TrueFoundry's dual-architecture matrix encompassing both an AI Gateway and a Deployment Orchestrator:
LlamaIndex agents combine TrueFoundry 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.
- Route LLM prompts securely utilizing a unified endpoint connecting to OpenAI, Anthropic, Gemini, Llama, and more
- Manage LLM Embeddings mapping strings flawlessly through secure unified channels
- Discover Gateway Models identifying exact runtime limitations and contexts
- Orchestrate MCP Containers deploying new AI server topology straight onto infrastructure limits
- Monitor Active Deployments generating status, usage array metrics, and isolation limits natively
- List MCP Schemas utilizing the managed TrueFoundry MCP discovery engine array
- Execute Chat streams dynamically routing user contexts purely bound without touching distinct API keys
The TrueFoundry 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 TrueFoundry to LlamaIndex via MCP
Follow these steps to integrate the TrueFoundry 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 TrueFoundry
Why Use LlamaIndex with the TrueFoundry MCP Server
LlamaIndex provides unique advantages when paired with TrueFoundry through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine TrueFoundry tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain TrueFoundry tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query TrueFoundry, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what TrueFoundry tools were called, what data was returned, and how it influenced the final answer
TrueFoundry + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the TrueFoundry MCP Server delivers measurable value.
Hybrid search: combine TrueFoundry real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query TrueFoundry 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 TrueFoundry for fresh data
Analytical workflows: chain TrueFoundry queries with LlamaIndex's data connectors to build multi-source analytical reports
TrueFoundry MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect TrueFoundry to LlamaIndex via MCP:
truefoundry_deploy_mcp_server
Spawn a new backend container logical process using TrueFoundry service mesh
truefoundry_generate_embeddings
Calculate semantic vectors securely using the unifed abstraction
truefoundry_get_deployment_status
Emit detailed metric states on the orchestration matrix bounds
truefoundry_get_mcp_server_info
Extract exact JSON metadata of one registered TrueFoundry tool schema
truefoundry_list_deployments
Monitor the existing array of running backend topologies mapped to the team
truefoundry_list_gateway_models
List all accessible foundation models from the TrueFoundry unified AI gateway
truefoundry_list_mcp_servers
Extract registry mapping of all available logical MCP Tools in TrueFoundry
truefoundry_run_gateway_chat
g., openai/gpt-4o) mapping the true chat parameter to the gateway. Perform inference explicitly pushing a model query string through TrueFoundry
Example Prompts for TrueFoundry in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with TrueFoundry immediately.
"List all active AI models supported natively inside my TrueFoundry gateway access instance."
"Trigger a chat payload pushing to 'openai-gpt4o' via TrueFoundry querying semantic structures bounding limits."
"Deploy the 'supabase-mcp' node-image natively mapping strict variables onto my cluster runtime boundaries."
Troubleshooting TrueFoundry MCP Server with LlamaIndex
Common issues when connecting TrueFoundry to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTrueFoundry + LlamaIndex FAQ
Common questions about integrating TrueFoundry 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 TrueFoundry 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 TrueFoundry to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.