Tinybird

# Tinybird MCP server

An MCP server to interact with a Tinybird Workspace from any MCP client.

Features

• Query Tinybird Data Sources using the Tinybird Query API
• Get the result of existing Tinybird API Endpoints with HTTP requests
• Push Datafiles

Usage examples

• [Bluesky metrics](https://bsky.app/profile/alasdairb.com/post/3lbx2mq5urk22) ([Claude transcript](https://www.tinybird.co/blog-posts/claude-analyze-bluesky-data-tinybird-mcp-server))
• [Web analytics starter kit metrics](https://github.com/tinybirdco/web-analytics-starter-kit) ([video](https://x.com/alrocar/status/1861849648882688341)\]

Setup

Installation

You can install the Tinybird MCP server using mcp-get:

```bash npx @michaellatman/mcp-get@latest install mcp-tinybird ```

Prerequisites

MCP is still very new and evolving, we recommend following the [MCP documentation](https://modelcontextprotocol.io/quickstart#prerequisites) to get the MCP basics up and running.

You'll need:

• [Tinybird Account & Workspace](https://www.tinybird.co/)
• [Claude Desktop](https://claude.ai/)
• [uv](https://docs.astral.sh/uv/getting-started/installation/)

Configuration

1. Configure Claude Desktop

Create the following file depending on your OS:

On MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`

On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

Paste this template in the file and replace `<TINYBIRD_API_URL>` and `<TINYBIRD_ADMIN_TOKEN>` with your Tinybird API URL and Admin Token:

```json { "mcpServers": { "mcp-tinybird": { "command": "uvx", "args": [ "mcp-tinybird" ], "env": { "TB_API_URL": "<TINYBIRD_API_URL>", "TB_ADMIN_TOKEN": "<TINYBIRD_ADMIN_TOKEN>" } } } } ```

2. Restart Claude Desktop

Prompts

The server provides a single prompt:

• [tinybird-default](https://github.com/tinybirdco/mcp-tinybird/blob/93dd9e1d3c0e33f408fe88297151a44c1dfc049c/src/mcp-tinybird/server.py#L20): Assumes you have loaded some data in Tinybird and want help exploring it.
  • Requires a "topic" argument which defines the topic of the data you want to explore, for example, "Bluesky data" or "retail sales".

You can configure additional prompt workflows:

• Create a prompts Data Source in your workspace with this schema and append your prompts. The MCP loads `prompts` on initialization so you can configure it to your needs: ```bash SCHEMA > `name` String `json:$.name`, `description` String `json:$.description`, `timestamp` DateTime `json:$.timestamp`, `arguments` Array(String) `json:$.arguments[:]`, `prompt` String `json:$.prompt` ```

Tools

The server implements several tools to interact with the Tinybird Workspace:

• `list-data-sources`: Lists all Data Sources in the Tinybird Workspace
• `list-pipes`: Lists all Pipe Endpoints in the Tinybird Workspace
• `get-data-source`: Gets the information of a Data Source given its name, including the schema.
• `get-pipe`: Gets the information of a Pipe Endpoint given its name, including its nodes and SQL transformation to understand what insights it provides.
• `request-pipe-data`: Requests data from a Pipe Endpoints via an HTTP request. Pipe endpoints can have parameters to filter the analytical data.
• `run-select-query`: Allows to run a select query over a Data Source to extract insights.
• `append-insight`: Adds a new business insight to the memo resource
• `llms-tinybird-docs`: Contains the whole Tinybird product documentation, so you can use it to get context about what Tinybird is, what it does, API reference and more.
• `save-event`: This allows to send an event to a Tinybird Data Source. Use it to save a user generated prompt to the prompts Data Source. The MCP server feeds from the prompts Data Source on initialization so the user can instruct the LLM the workflow to follow.
• `analyze-pipe`: Uses the Tinybird analyze API to run a ClickHouse explain on the Pipe Endpoint query and check if indexes, sorting key, and partition key are being used and propose optimizations suggestions
• `push-datafile`: Creates a remote Data Source or Pipe in the Tinybird Workspace from a local datafile. Use the [Filesystem MCP](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem) to save files generated by this MCP server.

Development

Config

If you are working locally add two environment variables to a `.env` file in the root of the repository:

```sh TB_API_URL= TB_ADMIN_TOKEN= ```

For local development, update your Claude Desktop configuration:

```json { "mcpServers": { "mcp-tinybird_local": { "command": "uv", "args": [ "--directory", "/path/to/your/mcp-tinybird", "run", "mcp-tinybird" ] } } } ```

<details> <summary>Published Servers Configuration</summary>

```json "mcpServers": { "mcp-tinybird": { "command": "uvx", "args": [ "mcp-tinybird" ] } } ``` </details>

Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile: ```bash uv sync ```

2. Build package distributions: ```bash uv build ```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI: ```bash uv publish ```

Note: You'll need to set PyPI credentials via environment variables or command flags:

• Token: `--token` or `UV_PUBLISH_TOKEN`
• Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).

You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash npx @modelcontextprotocol/inspector uv --directory /Users/alrocar/gr/mcp-tinybird run mcp-tinybird ```

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Monitoring

To monitor the MCP server, you can use any compatible Prometheus client such as [Grafana](https://grafana.com/). Learn how to monitor your MCP server [here](./mcp-analytics/README.md).