Honeycomb MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Honeycomb 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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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 Honeycomb. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Honeycomb?"
)
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 Honeycomb MCP Server
Connect your Honeycomb.io observability platform to any AI agent and take full control of your telemetry data, query specifications, and incident markers through natural conversation.
LlamaIndex agents combine Honeycomb tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Dataset Oversight — List all event sources, retrieve detailed metadata, and monitor last access times for your datasets.
- Query Management — Define new query specifications and execute them to retrieve granular performance insights.
- Marker Automation — Create timeline annotations (e.g., for deployments or outages) to contextualize your data visualization.
- Schema Insights — List and inspect columns within specific datasets to understand your event structure.
- Team Collaboration — Access shared boards and retrieve information about your Honeycomb team configuration.
- Incident Analysis — Use AI to run complex queries and retrieve results for rapid troubleshooting and RCA.
The Honeycomb MCP Server exposes 12 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 Honeycomb to LlamaIndex via MCP
Follow these steps to integrate the Honeycomb 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 12 tools from Honeycomb
Why Use LlamaIndex with the Honeycomb MCP Server
LlamaIndex provides unique advantages when paired with Honeycomb through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Honeycomb tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Honeycomb tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Honeycomb, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Honeycomb tools were called, what data was returned, and how it influenced the final answer
Honeycomb + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Honeycomb MCP Server delivers measurable value.
Hybrid search: combine Honeycomb real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Honeycomb 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 Honeycomb for fresh data
Analytical workflows: chain Honeycomb queries with LlamaIndex's data connectors to build multi-source analytical reports
Honeycomb MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Honeycomb to LlamaIndex via MCP:
create_marker
Pass details as a JSON string in "body_json" (requires message). Use "__all__" for team-wide markers. Create a new marker (e.g., deploy, maintenance) on a dataset timeline
create_query_specification
Pass the specification as a JSON string in "query_json". Returns a query ID for execution. Create a new query specification for a dataset
get_dataset_details
Get metadata for a specific dataset
get_query_result
Retrieve the results of an executed query
get_team_details
Retrieve information about the Honeycomb team
list_dataset_columns
List all columns (fields) defined in a specific dataset
list_datasets
Use this to find the "slug" required for markers and queries. List all datasets in your Honeycomb team
list_honeycomb_boards
List all boards (dashboards) shared with the team
list_markers
List markers (annotations) for a dataset
list_queries
List query specifications for a specific dataset
list_triggers
List triggers (alerts) defined for a dataset
run_query
Poll for results using "get_query_result" with the returned result ID. Execute a query specification and return a result ID
Example Prompts for Honeycomb in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Honeycomb immediately.
"List all datasets and find one related to 'payment-gateway'."
"Create a marker on all datasets: 'Deploy v2.4.0 started'."
"Execute query ID 'q_99283' for the 'main-api' dataset."
Troubleshooting Honeycomb MCP Server with LlamaIndex
Common issues when connecting Honeycomb to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHoneycomb + LlamaIndex FAQ
Common questions about integrating Honeycomb 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 Honeycomb 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 Honeycomb to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.