How to Use the FireHydrant MCP in LlamaIndex

Q: How do I connect this MCP Server to index retrospectives using LlamaIndex?

Use the llama-index-tools-mcp package to connect the server, then call listretrospectives to fetch past post-mortems. LlamaIndex can parse these documents into vector nodes, making past outage resolutions searchable by your SRE agent.

Q: Can LlamaIndex query live FireHydrant service dependencies?

Yes, your agent can call listservices and getservice to fetch live dependency maps. It can then index this structural data to answer complex questions about downstream blast radiuses during an outage.

Q: How does LlamaIndex prevent hallucinations when updating a FireHydrant incident?

LlamaIndex grounds the agent's actions by first querying getincident to pull the exact current state. The agent only calls updateincident or addincidentnote using the verified facts retrieved from the live API.

Q: Can I use this server to audit team assignments?

Yes, you can use LlamaIndex to query listteams and cross-reference active on-call schedules with past incident loads to optimize responder distribution.

Index FireHydrant incident data into LlamaIndex vector stores to ground your SRE agents in real historical context.

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Create your Vinkius account to connect FireHydrant to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Build a searchable historical database with LlamaIndex

Stop letting past outages go to waste. This MCP Server lets LlamaIndex query `list_retrospectives` and `list_incidents` to pull past post-mortems and index them into a local vector store. When a new issue arises, your agent can search this knowledge base to find how similar failures were resolved. By combining live data from `get_incident` with historical vector search, your SRE agent can suggest verified remediation steps instead of guessing. Outage resolution times drop when you put past team knowledge to work.

Query your live service catalog via LlamaIndex RAG

Keep your operational documentation fresh by querying live system state with this MCP server. Your LlamaIndex agent can call `list_services` and `get_service` to build an up-to-date index of your system architecture. When developers ask which team owns a failing microservice, the agent queries the index, pulls the owner via `get_team`, and provides the exact contact info. Manual lookups in stale spreadsheets are officially dead.

Automate incident documentation and updates

Maintain accurate incident timelines without manual typing. The agent reads system logs, matches them against recent deployments via `list_change_events`, and uses `add_incident_note` to document the findings. If the situation escalates, the agent uses `update_incident` to change the status or severity based on the real-time telemetry indexed during the outage. Coordination becomes cleaner when the timeline updates itself.

Setup guide

Set up FireHydrant MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all FireHydrant MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to FireHydrant tools.",
)
response = await agent.run("List recent FireHydrant data")

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Common questions about FireHydrant MCP in LlamaIndex

Use the `llama-index-tools-mcp` package to connect the server, then call `list_retrospectives` to fetch past post-mortems. LlamaIndex can parse these documents into vector nodes, making past outage resolutions searchable by your SRE agent.

Yes, your agent can call `list_services` and `get_service` to fetch live dependency maps. It can then index this structural data to answer complex questions about downstream blast radiuses during an outage.

LlamaIndex grounds the agent's actions by first querying `get_incident` to pull the exact current state. The agent only calls `update_incident` or `add_incident_note` using the verified facts retrieved from the live API.

Yes, you can use LlamaIndex to query `list_teams` and cross-reference active on-call schedules with past incident loads to optimize responder distribution.

The server only queries the specific service schemas and incident records you authorize. All data transfers occur within an isolated, zero-trust sandbox, ensuring your system architecture is never stored or used to train public models.

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