How do I get my Woodpecker API key?

Log in to your Woodpecker account at **app.woodpecker.co**. Navigate to **Settings** (gear icon). Click on **API keys** in the left menu. Click **Create new key**, give it a descriptive name, and copy the generated key. Paste it below. API access is available on all Woodpecker plans.

Can I automate multi-step follow-up sequences?

Yes. Woodpecker specializes in automated follow-up sequences. Your AI agent can create campaigns with up to 7 follow-up steps, each with customizable delays and personalization variables.

Does it protect my sender reputation?

Yes. Woodpecker includes built-in bounce detection, auto-blacklisting, and sending throttling. Your AI agent can monitor these safety metrics and alert you if deliverability drops below healthy thresholds.

Can I use it for agency clients?

Yes. Woodpecker offers an Agency plan with multi-client management. Each API key is scoped to your account, and you can manage campaigns for all clients from your AI agent.

Woodpecker MCP Server for LlamaIndex 8 tools — connect in under 2 minutes

Name: Woodpecker
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Woodpecker as an MCP tool provider through the 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 Woodpecker. "
            "You have 8 tools available."
        ),
    )

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

asyncio.run(main())

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* 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 Woodpecker MCP Server

Connect Woodpecker to your AI agent and manage your B2B cold email automation platform conversationally.

LlamaIndex agents combine Woodpecker tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through the 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

Campaign Management — Create, run, pause, and stop email campaigns with multi-step follow-up sequences.
Prospect Tracking — Add prospects, check reply statuses, and manage bounces and opt-outs.
Analytics — Pull open rates, click rates, reply rates, and bounce metrics per campaign.
Deliverability Monitoring — Track sending limits, warm-up progress, and inbox placement.

The Woodpecker MCP Server exposes 8 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 Woodpecker to LlamaIndex via MCP

Follow these steps to integrate the Woodpecker 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 8 tools from Woodpecker

Why Use LlamaIndex with the Woodpecker MCP Server

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

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

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

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

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

Woodpecker + LlamaIndex Use Cases

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

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

Data enrichment: query Woodpecker 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 Woodpecker for fresh data

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

Woodpecker MCP Tools for LlamaIndex (8)

These 8 tools become available when you connect Woodpecker to LlamaIndex via MCP:

add_prospect

Add prospect

get_campaign

Get campaign

get_campaign_stats

Get campaign stats

list_campaigns

List campaigns

list_prospects

List prospects

list_webhooks

List webhooks

pause_campaign

Pause campaign

resume_campaign

Resume campaign

Example Prompts for Woodpecker in LlamaIndex

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

"Show campaign stats for 'VP Engineering Outreach'."

"Add 20 new prospects to my active campaign."

"Who replied to my campaigns this week?"

Troubleshooting Woodpecker MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Woodpecker + LlamaIndex FAQ

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