Axiom MCP Server for LlamaIndexGive LlamaIndex instant access to 31 tools to Create Annotation, Create Dashboard, Create Dataset, and more

Name: Axiom
Price: 29 USD
Availability: InStock
Rating: 2.2 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Axiom 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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The Axiom MCP Server for LlamaIndex is a standout in the Data Analytics category — giving your AI agent 31 tools to work with, ready to go from day one.

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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 Axiom. "
            "You have 31 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Axiom?"
    )
    print(response)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Axiom MCP Server

Connect your Axiom account to any AI agent to streamline your observability and log management workflows through natural conversation.

LlamaIndex agents combine Axiom tool responses with indexed documents for comprehensive, grounded answers. Connect 31 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

Data Ingestion & Querying — Ingest JSON, NDJSON, or CSV data and run complex Axiom Processing Language (APL) queries to analyze logs in real-time.
Dataset Management — List, create, and update datasets to organize your telemetry and infrastructure data efficiently.
Monitoring & Alerts — Manage monitors and notifiers to stay informed about system performance, errors, and anomalies.
Dashboards & Annotations — Access dashboards and create annotations to visualize trends and mark significant system events.
Organization Insights — Retrieve user information, API tokens, and organization details to maintain secure and authorized access.

The Axiom MCP Server exposes 31 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 31 Axiom tools available for LlamaIndex

When LlamaIndex connects to Axiom through Vinkius, your AI agent gets direct access to every tool listed below — spanning telemetry, log-analysis, real-time-monitoring, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create annotation on Axiom

Create a new annotation

create

Create dashboard on Axiom

Create a new dashboard

create

Create dataset on Axiom

Create a new dataset

create

Create monitor on Axiom

Create a new monitor

create

Create notifier on Axiom

Create a new notifier

delete

Delete annotation on Axiom

Delete an annotation

delete

Delete dashboard on Axiom

Delete a dashboard

delete

Delete dataset on Axiom

Delete a dataset

delete

Delete monitor on Axiom

Delete a monitor

delete

Delete notifier on Axiom

Delete a notifier

get

Get annotation on Axiom

Retrieve a specific annotation by ID

get

Get dashboard on Axiom

Retrieve a specific dashboard by UID

get

Get dataset on Axiom

Retrieve a specific dataset by ID

get

Get monitor on Axiom

Retrieve a specific monitor by ID

get

Get notifier on Axiom

Retrieve a specific notifier by ID

get

Get org on Axiom

Retrieve an organization by ID

get

Get user on Axiom

Retrieve a specific user by ID

ingest

Ingest data on Axiom

Ingest data into an Axiom dataset

list

List annotations on Axiom

List all annotations

list

List dashboards on Axiom

List all dashboards

list

List datasets on Axiom

List all datasets

list

List monitors on Axiom

List all monitors

list

List notifiers on Axiom

List all notifiers

list

List tokens on Axiom

List all API tokens

list

List users on Axiom

List all users

run

Run query on Axiom

Run an APL query against Axiom data

update

Update annotation on Axiom

Update an existing annotation

update

Update dashboard on Axiom

Update an existing dashboard

update

Update dataset on Axiom

Update an existing dataset

update

Update monitor on Axiom

Update an existing monitor

update

Update notifier on Axiom

Update an existing notifier

Connect Axiom to LlamaIndex via MCP

Follow these steps to wire Axiom into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 31 tools from Axiom

Why Use LlamaIndex with the Axiom MCP Server

LlamaIndex provides unique advantages when paired with Axiom through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Axiom tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Axiom tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Axiom, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Axiom tools were called, what data was returned, and how it influenced the final answer

Axiom + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Axiom MCP Server delivers measurable value.

Hybrid search: combine Axiom real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Axiom 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 Axiom for fresh data

Analytical workflows: chain Axiom queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Axiom in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Axiom immediately.

"List all my available Axiom datasets."

"Run an APL query to count errors in 'production-logs' from the last 24 hours."

"Create a new monitor named 'High Latency' that checks for response times over 500ms."

Troubleshooting Axiom MCP Server with LlamaIndex

Common issues when connecting Axiom to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Axiom + LlamaIndex FAQ

Common questions about integrating Axiom 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 Axiom tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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