Autoplot AI MCP

An MCP (Model Context Protocol) server, managed with uv, that lets an MCP client discover public datasets (currently World Bank Open Data) by keyword — the first step of an automated data-fetching and visualization pipeline.

📌 Features

• uv for dependency management

• TOML + Pydantic based config, with API sources defined declaratively in configs/api_config.toml

• get_datasets_list tool — keyword search over a configured data source's datasets

• Multi-source ready: add new APIs by editing config, no code changes

• Centralized logging (loguru)

• Custom exception handling

• Standalone stdio client for manual testing

Related MCP server: DDG MCP1234

📁 Project Structure

The directory structure of the project looks like this:

├── LICENSE
├── Makefile
├── README.md
├── client.py
├── main.py
├── configs
│   ├── config.toml
│   └── api_config.toml
├── outputs
├── pyproject.toml
└── src
    ├── __init__.py
    ├── app.py
    ├── server
    │   ├── __init__.py
    │   └── server.py
    ├── tools
    │   ├── __init__.py
    │   ├── api_sources.py
    │   └── dataset_discovery.py
    └── utils
        ├── __init__.py
        ├── config.py
        ├── exceptions.py
        ├── logger.py
        └── models.py

🏗️ Architecture

main.py boots an App, which loads config and hands it to a Server that wraps FastMCP. Tool implementations live under src/tools/ (one module per tool, or tool family), each exposing a register(mcp, config) function; src/tools/__init__.py calls all of them so Server stays a thin wrapper. MCP clients (like client.py or the MCP Inspector) talk to the server over stdio; the tools in turn call out to whichever external data API is configured (e.g. the World Bank REST API).

flowchart LR
    subgraph Client
        C["client.py<br/>(MCP client)"]
    end

    subgraph "Autoplot AI MCP Server"
        M["main.py"] --> A["App"]
        A --> CFG["Config"]
        A --> S["Server<br/>(FastMCP)"]
        S --> R["tools.register_all"]
        R -->|registers| T["dataset_discovery.get_datasets_list"]
        T -->|resolves source| API["tools.api_sources.get_api_source"]
    end

    subgraph "Config Files"
        CT["config.toml<br/>(logger, server)"]
        AT["api_config.toml<br/>(API sources)"]
    end

    subgraph "External Data Sources"
        WB["World Bank Open Data API"]
    end

    CFG --> CT
    CFG --> AT
    C <-->|stdio / MCP protocol| S
    T -->|HTTP GET| WB

🔁 Sequence: get_datasets_list

sequenceDiagram
    participant U as User
    participant Cl as client.py
    participant Sv as Server (FastMCP)
    participant WB as World Bank API

    U->>Cl: Enter search text
    Cl->>Cl: Split input into keywords
    Cl->>Sv: call_tool("get_datasets_list", {keywords, source, limit})
    loop each result page
        Sv->>WB: GET discovery_endpoint (page, per_page)
        WB-->>Sv: indicators (name, sourceNote, id)
        Sv->>Sv: match keywords against name / description
    end
    Sv->>Sv: rank name-matches above description-matches,<br/>shorter names first
    Sv-->>Cl: top `limit` matching datasets
    Cl-->>U: print dataset id / name / description

🚀 Getting Started

Step 1: Install dependencies

uv sync

Step 2: Run the server

uv run python main.py
# or
make run

The server communicates over stdio and is meant to be launched by an MCP client (see Step 3), not run standalone in a terminal.

Step 3: Try it with the bundled client

uv run python client.py
# or
make client

This spawns main.py as a subprocess over stdio, lists the available tools, then prompts you for search text (e.g. population growth), splits it into keywords, and calls get_datasets_list to print matching datasets.

Step 4 (optional): Inspect it with the MCP Inspector

uv run mcp dev main.py:mcp

Opens a browser UI to browse and call the registered tools interactively. main.py exposes a lazily-built mcp attribute for this purpose (see __getattr__ at the bottom of the file) — normal runs via make run / make client don't trigger it.

⚙️ Configuration

• configs/config.toml — logger environment (dev / prod) and the MCP server's advertised name.

• configs/api_config.toml — a list of [[apis]], each describing an external data source: its name, description, discovery_endpoint (lists datasets), data_endpoint (downloads one dataset), response format, and per_page page size. Add a new source by appending another [[apis]] block — no code changes required.

📜 References

• Model Context Protocol

• MCP Python SDK

• uv

• World Bank Open Data API