How to Use the Woodpecker MCP in LlamaIndex

Q: How does LlamaIndex use Woodpecker tools?

LlamaIndex takes the output from MCP tools, like getcampaign or listprospects, and turns them into chunks for a vector store. This makes the data searchable later.

Q: Does the MCP Server support indexing webhook details with Woodpecker and LlamaIndex?

Yes. You can use listwebhooks to populate your index, creating a knowledge base of all active endpoints so you don't have to re-read documentation.

Q: What if I need to analyze multiple Woodpecker campaigns with LlamaIndex?

You can combine the output from listcampaigns and getcampaignstats into a single, unified index. This lets you ask comparative questions across different sequences.

Build searchable knowledge bases from Woodpecker data using LlamaIndex.

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Connect Woodpecker MCP to LlamaIndex

Create your Vinkius account to connect Woodpecker 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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Setup Woodpecker with LlamaIndex

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LlamaIndex: Campaign History Retrieval via MCP Server

Instead of just reading current stats, the agent uses `get_campaign_stats` and indexes those results. Later, you can query your knowledge base—'What were the open rates for Q1?'—and get an answer grounded in Woodpecker’s actual API data.

LlamaIndex: Prospect Data Search with MCP Server

You use `list_prospects` to pull a list of contacts, and LlamaIndex indexes those records. This means you can semantically search your past outreach data for specific types of leads or companies.

LlamaIndex: Campaign Status Querying

The agent calls `list_campaigns` to get a list, then indexes that full structure. You can build an application that answers 'Which campaigns were paused last month?' using the indexed historical data.

Setup guide

Set up Woodpecker 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 Woodpecker 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 Woodpecker tools.",
)
response = await agent.run("List recent Woodpecker data")

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Common questions about Woodpecker MCP in LlamaIndex

LlamaIndex takes the output from MCP tools, like `get_campaign` or `list_prospects`, and turns them into chunks for a vector store. This makes the data searchable later.

Yep. You can run a query against your index to pull up historical metrics from `get_campaign_stats`, even if you don't call the tool live.

Yes. You can use `list_webhooks` to populate your index, creating a knowledge base of all active endpoints so you don't have to re-read documentation.

You can combine the output from `list_campaigns` and `get_campaign_stats` into a single, unified index. This lets you ask comparative questions across different sequences.

This server handles prospect data (contact details) and campaign performance metrics. Make sure your RAG applications are designed to mask or generalize PII when indexing this information.

Use it with your favorite AI tools

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