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kaggle-mcp

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Kaggle MCP-Server (Model Context Protocol)

Dieses Repository enthält einen MCP-Server (Model Context Protocol) ( server.py ), der mit der Bibliothek fastmcp erstellt wurde. Er interagiert mit der Kaggle-API und bietet Tools zum Suchen und Herunterladen von Datensätzen sowie eine Eingabeaufforderung zum Generieren von EDA-Notebooks.

Projektstruktur

  • server.py : Die FastMCP-Serveranwendung. Sie definiert Ressourcen, Tools und Eingabeaufforderungen für die Interaktion mit Kaggle.

  • .env.example : Eine Beispieldatei für Umgebungsvariablen (Kaggle-API-Anmeldeinformationen). Benennen Sie die Datei in .env um und geben Sie Ihre Daten ein.

  • requirements.txt : Listet die erforderlichen Python-Pakete auf.

  • pyproject.toml & uv.lock : Projektmetadaten und gesperrte Abhängigkeiten für uv Paketmanager.

  • datasets/ : Standardverzeichnis, in dem heruntergeladene Kaggle-Datensätze gespeichert werden.

Related MCP server: Kaggle-MCP

Aufstellen

  1. Klonen Sie das Repository:

    git clone <repository-url>
cd <repository-directory>

  2. Erstellen Sie eine virtuelle Umgebung (empfohlen):

    python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
# Or use uv: uv venv

  3. Abhängigkeiten installieren: Verwenden von pip:

    pip install -r requirements.txt

    Oder mit UV:

    uv sync

  4. Richten Sie die Kaggle-API-Anmeldeinformationen ein:

    • Methode 1 (empfohlen): Umgebungsvariablen

      • Erstellen Sie .env Datei

      • Öffnen Sie die .env Datei und fügen Sie Ihren Kaggle-Benutzernamen und API-Schlüssel hinzu:

        KAGGLE_USERNAME=your_kaggle_username
KAGGLE_KEY=your_kaggle_api_key

      • Sie erhalten Ihren API-Schlüssel auf Ihrer Kaggle-Kontoseite ( Account > API > Create New API Token ). Dadurch wird eine kaggle.json Datei mit Ihrem Benutzernamen und Schlüssel heruntergeladen.

    • Methode 2: kaggle.json -Datei

      • Laden Sie Ihre kaggle.json Datei von Ihrem Kaggle-Konto herunter.

      • Platzieren Sie die Datei kaggle.json am erwarteten Speicherort (normalerweise ~/.kaggle/kaggle.json unter Linux/macOS oder C:\Users\<Your User Name>\.kaggle\kaggle.json unter Windows). Die kaggle Bibliothek erkennt diese Datei automatisch, wenn die Umgebungsvariablen nicht gesetzt sind.

Ausführen des Servers

  1. Stellen Sie sicher, dass Ihre virtuelle Umgebung aktiv ist.

  2. Führen Sie den MCP-Server aus:

    uv run kaggle-mcp

    Der Server wird gestartet und registriert seine Ressourcen, Tools und Eingabeaufforderungen. Sie können über einen MCP-Client oder kompatible Tools mit ihm interagieren.

Ausführen des Docker-Containers

1. Kaggle-API-Anmeldeinformationen einrichten

Für den Zugriff auf Kaggle-Datensätze sind für dieses Projekt Kaggle-API-Anmeldeinformationen erforderlich.

  • Gehen Sie zu https://www.kaggle.com/settings und klicken Sie auf „Neues API-Token erstellen“, um Ihre kaggle.json Datei herunterzuladen.

  • Öffnen Sie die Datei kaggle.json und kopieren Sie Ihren Benutzernamen und Schlüssel in eine neue .env Datei im Projektstamm:

KAGGLE_USERNAME=your_username
KAGGLE_KEY=your_key

2. Erstellen Sie das Docker-Image

docker build -t kaggle-mcp-test .

3. Führen Sie den Docker-Container mit Ihrer .env-Datei aus

docker run --rm -it --env-file .env kaggle-mcp-test

Dadurch werden Ihre Kaggle-Anmeldeinformationen automatisch als Umgebungsvariablen in den Container geladen.

Serverfunktionen

Der Server stellt über das Model Context Protocol die folgenden Funktionen bereit:

Werkzeuge

  • search_kaggle_datasets(query: str) :

    • Sucht auf Kaggle nach Datensätzen, die der angegebenen Abfragezeichenfolge entsprechen.

    • Gibt eine JSON-Liste der 10 am besten übereinstimmenden Datensätze mit Details wie Referenz, Titel, Downloadanzahl und Datum der letzten Aktualisierung zurück.

  • download_kaggle_dataset(dataset_ref: str, download_path: str | None = None) :

    • Lädt Dateien für einen bestimmten Kaggle-Datensatz herunter und entpackt sie.

    • dataset_ref : Die Dataset-Kennung im Format username/dataset-slug (z. B. kaggle/titanic ).

    • download_path (optional): Gibt an, wohin der Datensatz heruntergeladen werden soll. Wenn dieser Pfad weggelassen wird, wird standardmäßig ./datasets/<dataset_slug>/ relativ zum Speicherort des Serverskripts verwendet.

Eingabeaufforderungen

  • generate_eda_notebook(dataset_ref: str) :

    • Generiert eine für ein KI-Modell (wie Gemini) geeignete Eingabeaufforderung, um ein grundlegendes EDA-Notebook (Exploratory Data Analysis) für die angegebene Kaggle-Datensatzreferenz zu erstellen.

    • Die Eingabeaufforderung verlangt Python-Code zum Laden von Daten, zur Überprüfung fehlender Werte, zu Visualisierungen und zu grundlegenden Statistiken.

Verbindung zu Claude Desktop herstellen

Gehen Sie zu Claude > Einstellungen > Entwickler > Konfiguration bearbeiten > claude_desktop_config.json, um Folgendes einzuschließen:

{
  "mcpServers": {
    "kaggle-mcp": {
      "command": "kaggle-mcp",
      "cwd": "<path-to-their-cloned-repo>/kaggle-mcp"
    }
  }
}

Anwendungsbeispiel

Ein KI-Agent oder MCP-Client könnte folgendermaßen mit diesem Server interagieren:

  1. Agent: „Durchsuchen Sie Kaggle nach Datensätzen zum Thema ‚Herzkrankheiten‘“

    • Der Server führt search_kaggle_datasets(query='heart disease') aus.

  2. Agent: „Laden Sie den Datensatz ‚user/heart-disease-dataset‘ herunter.“

    • Der Server führt download_kaggle_dataset(dataset_ref='user/heart-disease-dataset') aus.

  3. Agent: „Generieren Sie eine EDA-Notebook-Eingabeaufforderung für ‚user/heart-disease-dataset‘“

    • Der Server führt generate_eda_notebook(dataset_ref='user/heart-disease-dataset') aus.

    • Der Server gibt eine strukturierte Eingabeaufforderungsnachricht zurück.

  4. Agent: (Sendet die Eingabeaufforderung an ein Code generierendes Modell) -> Empfängt EDA-Python-Code.

Available Tools

2 tools
download_kaggle_datasetC

Downloads files for a specific Kaggle dataset. Args: dataset_ref: The reference of the dataset (e.g., 'username/dataset-slug'). download_path: Optional. The path to download the files to. Defaults to '/datasets/'.

ParametersJSON Schema
NameRequiredDescriptionDefault
dataset_refYes
download_pathNo

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the action but lacks critical details: whether authentication is required (Kaggle typically needs API credentials), what happens if files already exist at the path, error handling, or any rate limits. The description is minimal beyond the basic operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with a clear purpose statement followed by parameter explanations. It avoids unnecessary fluff, though the formatting with 'Args:' could be more integrated. Every sentence adds value, making it appropriately concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of downloading datasets (which often involves authentication, file management, and error cases), no annotations, and no output schema, the description is insufficient. It misses key contextual details like authentication requirements, response format, or handling of large downloads, leaving significant gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context for both parameters: it explains the format of 'dataset_ref' with an example and clarifies the default behavior and path structure for 'download_path'. With 0% schema description coverage, this compensates somewhat, but it doesn't fully detail constraints (e.g., path validity, dataset accessibility).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Downloads files') and resource ('for a specific Kaggle dataset'), making the purpose immediately understandable. It distinguishes from the sibling tool 'search_kaggle_datasets' by focusing on downloading rather than searching, though it doesn't explicitly contrast them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. While it's implied this is for downloading after a dataset is identified (versus searching with the sibling tool), there's no explicit mention of prerequisites, dependencies, or when-not-to-use scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_kaggle_datasetsC

Searches for datasets on Kaggle matching the query using the Kaggle API.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions using the Kaggle API but doesn't disclose behavioral traits such as authentication requirements, rate limits, pagination, or what the search returns (e.g., format, fields). This leaves significant gaps for an agent to understand how to use it effectively.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't cover key aspects like authentication, rate limits, return format, or error handling. For a search tool with no structured support, more context is needed to guide an agent effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It implies the 'query' parameter is used for searching datasets, but doesn't add meaning beyond what the schema's title ('Query') and type suggest. No details on query syntax, examples, or constraints are provided, resulting in minimal added value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Searches for datasets') and target resource ('on Kaggle'), specifying it uses the Kaggle API. It distinguishes from the sibling tool 'download_kaggle_dataset' by focusing on search rather than download, though it doesn't explicitly mention this distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention the sibling tool 'download_kaggle_dataset' or any other search methods, nor does it specify prerequisites like authentication or rate limits.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one downloads a specific dataset, while the other searches for datasets. There is no overlap in functionality, making it easy for an agent to choose the correct tool for each task without confusion.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (download_kaggle_dataset and search_kaggle_datasets), using snake_case and clear action verbs. This consistency makes the tool set predictable and easy to understand at a glance.

Tool Count2/5

With only two tools, the server feels thin for a Kaggle integration, lacking essential operations like listing datasets, uploading data, or managing competitions. While the tools are functional, the scope is incomplete for typical Kaggle workflows, making the count too low for the domain.

Completeness2/5

The tool set is severely incomplete for a Kaggle MCP server. It covers downloading and searching datasets but misses critical operations such as uploading datasets, accessing competition data, or interacting with notebooks. This creates significant gaps that will hinder agents from performing common Kaggle tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

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  • Kaggle MCP Server

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