How do I create a Pingdom API Token?

In your Pingdom dashboard (or SolarWinds portal), go to **Settings** > **API Tokens**. Click **Add API Token**, give it a name, and ensure it has the necessary access levels.

Can I see response times for different global regions?

Yes! The `list_check_results` tool returns data from multiple probe locations, allowing you to see how your site performs from different parts of the world.

Does this support pausing multiple checks at once?

The tools handle checks individually, but you can ask the AI agent to 'Pause all checks related to our API' and it will process them sequentially using their IDs.

Pingdom MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Pingdom
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 Pingdom 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 Pingdom. "
            "You have 10 tools available."
        ),
    )

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

asyncio.run(main())

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About Pingdom MCP Server

Connect your Pingdom account to any AI agent and take full control of your website monitoring and reliability workflows through natural conversation.

LlamaIndex agents combine Pingdom 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

Uptime Visibility — List all monitoring checks and retrieve real-time status (up, down, unconfirmed).
Performance Tracking — Fetch average response times and detailed outage history for any specific check.
Log Auditing — Retrieve raw check results to investigate specific errors or latency spikes.
Global Infrastructure Oversight — List all Pingdom probe locations to understand your monitoring coverage.
Alert Management — List notification contacts and pause or resume checks during maintenance windows.

The Pingdom 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 Pingdom to LlamaIndex via MCP

Follow these steps to integrate the Pingdom 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 Pingdom

Why Use LlamaIndex with the Pingdom MCP Server

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

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

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

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

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

Pingdom + LlamaIndex Use Cases

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

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

Data enrichment: query Pingdom 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 Pingdom for fresh data

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

Pingdom MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Pingdom to LlamaIndex via MCP:

get_average_response_time

Get average response time for a check

get_check_details

Get details for a specific check

get_check_outages

List outages for a specific check

list_alert_contacts

List alert notification contacts

list_check_results

List individual check results/logs

list_maintenance_windows

List scheduled maintenance windows

list_pingdom_probes

List all Pingdom monitoring locations (probes)

list_uptime_checks

List all Pingdom uptime checks

pause_uptime_check

Pause a specific uptime check

resume_uptime_check

Resume a specific uptime check

Example Prompts for Pingdom in LlamaIndex

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

"List all my current uptime checks and their status."

"What was the average response time for the 'Main Site' check (ID: 12345) today?"

"Pause the uptime check for ID 98765 for our scheduled maintenance."

Troubleshooting Pingdom MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Pingdom + LlamaIndex FAQ

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