> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bananaflow.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# MCP Server

> Connect Codex, Claude, and other AI assistants to your Bananaflow workspace via the Model Context Protocol

## Overview

Bananaflow exposes an [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) server that lets AI assistants like Codex, Claude Code, Claude Desktop, and Cursor access your workspace and documentation. Once connected, an assistant can list your agents, fetch agent definitions, and search Bananaflow docs on your behalf.

## Prerequisites

* A Bananaflow API key. Generate one at [`/api-keys`](https://app.bananaflow.ai/api-keys). See [API Keys](/configurations/api-keys).
* Your Bananaflow MCP endpoint: `https://app.bananaflow.ai/api/v1/mcp/`

The endpoint is also shown in **Platform Settings → MCP Server** inside the Bananaflow UI.

## Claude Code

Register Bananaflow as an MCP server with the Claude Code CLI:

```bash theme={null}
claude mcp add --transport http bananaflow https://app.bananaflow.ai/api/v1/mcp/ \
  --header "X-API-Key: YOUR_API_KEY"
```

Replace `YOUR_API_KEY` with the key you generated.

Verify the server is connected:

```bash theme={null}
claude mcp list
```

## Codex

Open Codex's config file (`~/.codex/config.toml`) and add a `bananaflow` MCP server:

```toml theme={null}
[mcp_servers.bananaflow]
url = "https://app.bananaflow.ai/api/v1/mcp/"
http_headers = { "X-API-Key" = "YOUR_API_KEY" }
```

Replace `YOUR_API_KEY` with the key you generated.

If you prefer to keep the API key out of `config.toml`, store it in an environment variable instead:

```toml theme={null}
[mcp_servers.bananaflow]
url = "https://app.bananaflow.ai/api/v1/mcp/"
env_http_headers = { "X-API-Key" = "BANANAFLOW_API_KEY" }
```

Then set the API key before starting Codex:

```bash theme={null}
export BANANAFLOW_API_KEY="YOUR_API_KEY"
codex
```

Verify the server is registered:

```bash theme={null}
codex mcp list
codex mcp get bananaflow
```

## Claude Desktop

Open Claude Desktop's config file (`claude_desktop_config.json`) and add the `bananaflow` entry under `mcpServers`:

```json theme={null}
{
  "mcpServers": {
    "bananaflow": {
      "url": "https://app.bananaflow.ai/api/v1/mcp/",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}
```

Restart Claude Desktop after saving. The Bananaflow tools should appear in the tool picker.

## Cursor and other MCP clients

Any MCP client that supports Streamable HTTP transport can connect with the same URL and header. Paste the configuration above into your client's MCP settings file and replace `YOUR_API_KEY`.

## Example prompts

Once the MCP server is connected, you can drive Bananaflow from your coding agent in plain English. A few prompts to try:

**Explore your workspace**

* "List my agents in Bananaflow."
* "Show me the definition of the agent called *Lead Qualifier*."
* "Which credentials and tools are configured in my Bananaflow workspace?"
* "List the recordings from my most recent agent."

**Edit an agent**

* "In my *Lead Qualifier* agent, add a new agent node after the greeting that asks the caller for their budget, then routes to the existing qualification node."
* "Add an end-call node to *Support Bot* that triggers when the user says they are done, with a polite goodbye prompt."
* "Rename the *intro* node in *Lead Qualifier* to *greeting* and update any edges that reference it."

**Learn the platform**

* "What node types does Bananaflow support, and what fields does a `knowledge_base` node take?"

<Note>
  Agent edits are saved as a new **draft** version – your published agent keeps serving calls until you explicitly publish the draft from the Bananaflow UI.
</Note>

<Note>
  The API key controls which workspace the assistant sees. Treat it like any other credential – do not commit it to source control or paste it into shared chats.
</Note>


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