Can I check the budget and rate limits for a specific proxy key?

Yes. Use the `get_key_info` tool with the specific Key ID. Your agent will retrieve the exact rate limits, budget constraints, and current RPM usage associated with that token.

How do I see the model fallback paths configured in my proxy?

The `get_model_info` tool allows your agent to extract the global model directory. You'll see the exact fallback chains (e.g., if OpenAI fails, use Anthropic) and the physical endpoints assigned to each model name.

Can my agent create a new team to track specific division costs?

Absolutely. Use the `create_team` tool and provide a JSON payload defining the team name and optional budget limits. Your agent will provision the new team identity in LiteLLM, allowing for precise organizational cost tracking.

LiteLLM (LLM Proxy & Spend Tracking) MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: LiteLLM (LLM Proxy & Spend Tracking)
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LiteLLM (LLM Proxy & Spend Tracking) 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 LiteLLM (LLM Proxy & Spend Tracking). "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in LiteLLM (LLM Proxy & Spend Tracking)?"
    )
    print(response)

asyncio.run(main())

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About LiteLLM (LLM Proxy & Spend Tracking) MCP Server

Connect your LiteLLM Proxy instance to any AI agent and take full control of your LLM infrastructure, load balancing, and spend management through natural conversation.

LlamaIndex agents combine LiteLLM (LLM Proxy & Spend Tracking) 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

Key Orchestration — Generate and manage proxy API keys to isolate distinct microservices or teams, including precise budget and rate limit constraints directly from your agent
Model Routing Intelligence — Get detailed info on fallback paths (e.g., OpenAI -> Anthropic -> Groq) and verify exact routing endpoints assigned to your models
Real-time Spend Audit — Track total USD consumed by specific end-users or teams and monitor budget ceilings to ensure cost-effective AI deployments
Dynamic Model Control — Inject fresh routing endpoints (e.g., new AWS Bedrock or Azure OpenAI deployments) into your proxy runtime with zero downtime
Team & Organizational Isolation — Create and manage team profiles to track exact cost limits and operational boundaries per organizational division
Infrastructure Security — Instantly vaporize malicious or leaked keys and remove broken LLM deployments to prevent downstream 500 errors dynamically

The LiteLLM (LLM Proxy & Spend Tracking) 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 LiteLLM (LLM Proxy & Spend Tracking) to LlamaIndex via MCP

Follow these steps to integrate the LiteLLM (LLM Proxy & Spend Tracking) 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 LiteLLM (LLM Proxy & Spend Tracking)

Why Use LlamaIndex with the LiteLLM (LLM Proxy & Spend Tracking) MCP Server

LlamaIndex provides unique advantages when paired with LiteLLM (LLM Proxy & Spend Tracking) through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine LiteLLM (LLM Proxy & Spend Tracking) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain LiteLLM (LLM Proxy & Spend Tracking) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query LiteLLM (LLM Proxy & Spend Tracking), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what LiteLLM (LLM Proxy & Spend Tracking) tools were called, what data was returned, and how it influenced the final answer

LiteLLM (LLM Proxy & Spend Tracking) + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the LiteLLM (LLM Proxy & Spend Tracking) MCP Server delivers measurable value.

Hybrid search: combine LiteLLM (LLM Proxy & Spend Tracking) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query LiteLLM (LLM Proxy & Spend Tracking) 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 LiteLLM (LLM Proxy & Spend Tracking) for fresh data

Analytical workflows: chain LiteLLM (LLM Proxy & Spend Tracking) queries with LlamaIndex's data connectors to build multi-source analytical reports

LiteLLM (LLM Proxy & Spend Tracking) MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect LiteLLM (LLM Proxy & Spend Tracking) to LlamaIndex via MCP:

create_model

Inject completely fresh routing endpoints (ex: new Bedrock Llama 4 endpoints)

create_team

Generate pristine organizational isolation tracking exact cost limits per division

create_user

Insert specific End-User identities bridging Vinkius with Proxy logs

delete_key

Delete an existing LLM proxy key entirely

delete_model

Delete explicitly routed LLM deployments preventing 500s dynamically

generate_key

Generate a new proxy API key isolating distinct microservices or teams

get_key_info

Get configuration and budget bounds for a specific LiteLLM API Key

get_model_info

Get array endpoints tracing exact Fallback paths like OpenAI -> Anthropic

get_team_info

Get internal logic bounds matching multiple routing users via Team UUID

get_user_info

Return precise End-User abstractions tracking total USD consumed natively

Example Prompts for LiteLLM (LLM Proxy & Spend Tracking) in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with LiteLLM (LLM Proxy & Spend Tracking) immediately.

"List all active model fallback paths in LiteLLM"

"Generate a new API key for the 'Customer-Service' team with a $50 monthly budget"

"How much has user 'alex_dev' spent on LLM tokens today?"

Troubleshooting LiteLLM (LLM Proxy & Spend Tracking) MCP Server with LlamaIndex

Common issues when connecting LiteLLM (LLM Proxy & Spend Tracking) to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

LiteLLM (LLM Proxy & Spend Tracking) + LlamaIndex FAQ

Common questions about integrating LiteLLM (LLM Proxy & Spend Tracking) 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 LiteLLM (LLM Proxy & Spend Tracking) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.