Poe MCP for AI. Manage AI Bots and Chain Model Responses from Code.

Q: How do I list all my bots using the Poe MCP Server?

You call listbots. This tool returns an array containing the name, handle, and status of every single bot configured under your Poe account.

Q: What is querybot used for in a workflow?

querybot allows you to run one specific bot (e.g., Claude) from another part of your code, using that bot's output as direct input data for the next step.

Q: Can I test my bot without a real user?

Yes. Use sendmessage. This tool simulates a user sending a message to the specified bot endpoint, letting you verify connectivity and response quality without generating actual history.

Q: What if I need to change my bot's settings?

Use updatebot. You provide the new configuration parameters (like a revised system prompt or model type) and the changes take effect immediately for all future conversations.

Q: How do I debug a bot that is giving unexpected responses using listmessages?

Use listmessages to view recent conversations. This returns message content, timestamps, and user IDs, letting you analyze the exact input or response data for debugging.

Q: Does getbotstats help me monitor performance bottlenecks?

Yes, getbotstats provides usage statistics. You can track message counts, unique users, and average response times to spot where your bot needs optimization.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

The Poe MCP Server manages your AI chatbot infrastructure directly through API calls. Use this to list, create, update, and delete bots on the Poe platform.

You can also monitor conversation history with `list_messages`, track usage stats via `get_bot_stats`, or chain responses between different models using `query_bot`.

What your AI can do

Create bot

Creates a new API bot on Poe using specified parameters like name, model, and base URL.

Delete bot

Permanently removes an existing Poe API bot. Be careful; this action loses all history and settings.

List available bots

Lists all publicly available bots on the Poe platform that your own bots can connect to and query.

+ 7 more capabilities included

Manage Bot Configurations

Create, update, or delete Poe API bots programmatically to control their settings.

Chain AI Model Responses

Run a bot query through multiple different models (GPT-4, Claude, etc.) and use the output from one model as input for the next.

Monitor Conversations

View detailed message logs and timestamps using list_messages to debug user interactions or analyze patterns.

Track Usage Metrics

Get quantitative data on bot performance, including total messages sent and unique users engaged with the bot (get_bot_stats).

Test Endpoints

Send simulated test messages using send_message to validate that a bot is correctly connected and responding.

Ask an AI about this

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AI Agent

Poe MCP Server: 10 Tools for Bot Management & Orchestration

These ten tools let your AI agent manage the full lifecycle of bots on Poe—from creation to querying and performance tracking.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Poe on Vinkius

Create Bot

Creates a new API bot on Poe using specified parameters like name, model, and base URL.

Delete Bot

Permanently removes an existing Poe API bot. Be careful; this action loses all...

List Available Bots

Lists all publicly available bots on the Poe platform that your own bots can connect...

Get Bot Stats

Pulls usage statistics—like message counts and unique users—for any specified Poe...

Get Bot

Retrieves the detailed configuration and metadata for a single, specific Poe bot ID.

List Bots

Returns a comprehensive list of every bot you own under your connected Poe account.

List Messages

Fetches the recent conversation history, showing message content and timestamps for debugging or analysis.

Query Bot

Runs a specified bot on Poe, allowing you to chain its response into your current...

Send Message

Simulates a user sending a message to a Poe bot endpoint for quick connectivity...

Update Bot

Changes the configuration settings of an existing Poe bot, applying changes...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Poe integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "poe": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Poe tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"poe": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Poe, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Poe. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Checking bot performance shouldn't require jumping between five different dashboards.

Today, tracking an AI bot’s health means logging into the Poe dashboard. You have to navigate away from your main workflow, check the message tab for recent activity, and then find a separate 'Stats' section just to count unique users or total messages. It's clicks, copies, and context switching.

With this MCP server, all that data is available via API calls. Just run `list_messages` to see conversation details and use `get_bot_stats` for hard metrics—all from a single command line. You get the full operational picture without leaving your code editor.

Using Poe MCP Server: Chain responses between models in minutes.

Before this, if you wanted Model A to summarize data and then use that summary for a tone check by Model B, you had to export the output from one service and manually paste it into another. The process was slow, fragile, and prone to human error.

Now, your agent handles the whole thing: Model A runs, its structured response is captured, and then `query_bot` passes that exact data directly to Model B. It's a seamless, automated handoff from one AI model to the next.

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What your AI can actually do with this

You're dealing with Poe—you need your AI agent to talk to it like it's just another service, not something you have to log into via a web dashboard. This server lets you manage and execute against all those bots programmatically. You don't wanna manually mess around on the site; you want your code to handle the whole thing.

Managing Your Bot Infrastructure

You can control the entire lifecycle of any Poe API bot using specific functions. Need a new bot? Use create_bot and specify all the parameters—the name, the model type, and the base URL—to get it set up right in your code. If you've got an existing bot that needs tweaks, run update_bot; it applies those changes immediately for any conversations that follow.

When a bot is dead weight or just obsolete, use delete_bot to pull it off the platform completely; remember, though, this action wipes out all history and settings, so you gotta be careful.

To check what's already running, run list_bots, which gives you a complete list of every bot under your connected Poe account. If you only care about one specific setup, use get_bot with the ID to pull up its detailed configuration and metadata for a quick review. You can also peep at all the publicly available bots on the platform that your own systems can connect to using list_available_bots.

Running Queries and Chaining Models

This is where the real power is. Forget running queries manually; you'll use query_bot to run a specified bot on Poe, allowing you to pull its response directly into your workflow. This lets you chain responses—say, getting GPT-4 to summarize a document, and then feeding that summary right into Claude for refinement or tone adjustment.

You can also test connectivity fast by running send_message, which simulates a user sending an actual message to the bot endpoint. If you want to just verify if a whole sequence of models works together, this function validates that connection point.

Monitoring and Debugging Data

You need hard data on how your bots are doing, right? You can pull usage statistics for any specified Poe bot using get_bot_stats. This gives you quantitative metrics like the total message count and the number of unique users who've actually interacted with it. If a bot starts acting weird or misses context, don't guess—use list_messages to fetch the recent conversation history.

It shows you every message content and the timestamps for serious debugging or if you just wanna analyze usage patterns.

The Bottom Line

In short, your agent uses this server to handle everything: it builds bots using create_bot, modifies them with update_bot or removes 'em with delete_bot; it runs complex logic by chaining models through query_bot; and you can track the whole performance picture—from message counts via get_bot_stats to specific conversation logs using list_messages.

You've got total programmatic control over your Poe chatbot infrastructure.

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Server ID 019d846f-3ff7-72c5-8812-23100cc6ff8c

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Compliance Grade A

Score 94.17/100

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Here's how it actually works

The bottom line is that you treat Poe as a managed API service, allowing your agent to perform all necessary bot administrative tasks without manual web interaction.

First, subscribe to the server and give your AI client the Poe API access token.

Then, use an agent command (e.g., 'List all my bots') to call the list_bots tool.

Finally, you get a structured list of every bot ID, name, and status configured under your account.

Who is this actually for?

This tool targets developers and researchers who build complex AI applications. If your job requires managing multiple third-party LLM integrations or building bots that talk to other models, you need this. It's for the engineer tired of juggling dashboards just to check a message count.

Bot Developer

Uses create_bot and update_bot to deploy new bots or modify existing ones directly from code.

AI Researcher

Employs query_bot repeatedly to run comparative tests, comparing outputs across different models (e.g., GPT-4 vs Claude).

DevOps Engineer

Runs checks like get_bot_stats and list_messages regularly to monitor system health and spot performance bottlenecks.

What Changes When You Connect

Full Bot Lifecycle Control: You gain the ability to programmatically manage every bot. Instead of logging in to create a new service, use create_bot or update_bot directly through your agent's API call.

Cross-Model Logic (Chaining): Stop running models in silos. Use query_bot to build complex logic where Model A generates data and passes that result for refinement by Model B.

Immediate Debugging: When a bot fails, you don't have to guess why. Run list_messages to pull the full conversation history, see timestamps, and debug exactly where the interaction broke down.

Performance Benchmarking: Need to know if your bot is underutilized? Use get_bot_stats. It gives metrics like message counts and unique users—hard numbers you need for product reporting.

Zero-Friction Testing: Before deploying a new workflow, use send_message to fire off a test payload. This validates the endpoint connection instantly without simulating an actual user interaction.

See it in action

01 01

Analyzing Support Ticket Responses

The Community Manager needs to audit if their 'SupportBot' is keeping up. They use list_messages to pull the last 50 conversations, then run get_bot_stats to see if message volume spiked this week, identifying potential resource bottlenecks.

02 02

Building a Research Pipeline

An AI Researcher wants to compare how GPT-4 and Claude summarize the same text. The agent first uses list_available_bots to identify both models, then runs query_bot twice—once for each model—to get comparable outputs in a single workflow.

03 03

Automated Bot Deployment

A Bot Developer finishes a new bot concept. They use create_bot to spin up the initial version, then immediately use update_bot to set the final system prompt and test it with send_message before going live.

04 04

Auditing Account Status

A DevOps Engineer needs a quick inventory. They run list_bots to see everything owned, then use get_bot on the most critical bot to verify its current model settings and endpoint URL.

The honest tradeoffs

Relying only on chat interfaces

Anti-pattern

Manually opening the Poe website, clicking through tabs, copying data points for usage statistics or conversation logs.

The Fix

Use list_messages to pull all history in one API call. Use get_bot_stats to get clean, numerical metrics without navigating dashboards.

Assuming a bot is active

Anti-pattern

Writing complex logic that assumes the target bot exists and works, only to fail hours later when a setting changes.

The Fix

Always run list_bots first. If you need metrics, confirm existence with get_bot before attempting any action.

Debugging via manual guessing

Anti-pattern

A bot fails and the developer spends an hour trying to guess if it's a prompt issue or a connectivity problem.

The Fix

Use send_message first. If that works, run list_messages to see the exact payload and response data captured by the API.

When It Fits, When It Doesn't

You should use this server if your primary task is managing multiple AI services—especially when you need to programmatically compare or sequence responses between different models. The key function here is orchestration, not just simple chat. Use it if you need to build a multi-step pipeline (e.g., Step 1: query_bot on GPT for data; Step 2: Pass data and query_bot on Claude for analysis). Don't use this if all your needs are contained within one single, isolated AI service instance—in that case, a simple API key connection might suffice. If you only need to check status occasionally, writing a script around the list_bots and get_bot_stats tools is faster than building a full client.

Questions you might have

How do I list all my bots using the Poe MCP Server? +

You call list_bots. This tool returns an array containing the name, handle, and status of every single bot configured under your Poe account.

What is `query_bot` used for in a workflow? +

query_bot allows you to run one specific bot (e.g., Claude) from another part of your code, using that bot's output as direct input data for the next step.

Can I test my bot without a real user? +

Yes. Use send_message. This tool simulates a user sending a message to the specified bot endpoint, letting you verify connectivity and response quality without generating actual history.

What if I need to change my bot's settings? +

Use update_bot. You provide the new configuration parameters (like a revised system prompt or model type) and the changes take effect immediately for all future conversations.

What API token do I need to use the `list_bots` tool? +

You must provide your Poe API access token from creator.poe.com. This token authenticates your connection and allows the agent to list all bots associated with your account.

How do I debug a bot that is giving unexpected responses using `list_messages`? +

Use list_messages to view recent conversations. This returns message content, timestamps, and user IDs, letting you analyze the exact input or response data for debugging.

Does `get_bot_stats` help me monitor performance bottlenecks? +

Yes, get_bot_stats provides usage statistics. You can track message counts, unique users, and average response times to spot where your bot needs optimization.

What is the risk of using the `delete_bot` tool? +

Be careful; this action removes a bot permanently. The API call deletes all associated conversation history and settings, so double-check before running it.

How do I get a Poe API access token? +

Go to creator.poe.com, navigate to Settings > API, and generate an access token. This token authenticates all API requests for bot management.

What AI models can I query through Poe? +

Poe has GPT-4, GPT-4o, Claude 3.5 Sonnet, Claude 3 Opus, Llama 3, and dozens of specialized bots. Use query_bot to chain responses from multiple models in your workflows.

How does bot chaining work? +

Using Poe API v2, your bot can query any other bot on Poe for free. The response becomes input for your bot's processing. Costs are covered by the user's free message limit or subscription.

Connect to your AI in seconds.

Create bot

Delete bot

List available bots

Poe MCP Server: 10 Tools for Bot Management & Orchestration

Make your AI actually useful.

Create Bot

Delete Bot

List Available Bots

Get Bot Stats

Get Bot

List Bots

List Messages

Query Bot

Send Message

Update Bot

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Checking bot performance shouldn't require jumping between five different dashboards.

Using Poe MCP Server: Chain responses between models in minutes.

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Analyzing Support Ticket Responses

Building a Research Pipeline

Automated Bot Deployment

Auditing Account Status

The honest tradeoffs

Relying only on chat interfaces

Assuming a bot is active

Debugging via manual guessing

When It Fits, When It Doesn't

Questions you might have