DataClue — AI training data

Search and license real-world AI training data: egocentric video, UMI gripper demos, custom EEG.

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Calidad y seguridad

A
Calidad de la descripción
100%
Integridad del esquema
83%
Calidad de los nombres
95%
Riesgo de envenenamiento
100%
Coincidencia de permisos
100%
Cumplimiento del protocolo
100%

Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.

Costo de contexto

~832Tokens (definiciones de herramientas)
~1.3 KBTamaño de respuesta típico
Impacto moderado en la atención (0.65% del contexto de 128k)

Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.

Instalar

Instalación con un clic

Agrega esto a tu archivo `claude_desktop_config.json`:

{
  "mcpServers": {
    "catalog": {
      "url": "https://dataclue.app/mcp"
    }
  }
}

Puntos de conexión remotos

https://dataclue.app/mcpstreamable-http

Qué puede hacer

Inventario de herramientas

Herramientas (4)

🟢 Solo lectura🟡 Escritura🔴 Eliminación⚪ Desconocido
🟢search_datasets(query)

Search DataClue's catalog of licensable AI training data (egocentric video of real work, UMI gripper demonstrations) with a plain-English need, e.g. "50,000 hours of household manipulation video, 1080p, long sessions". Returns matching datasets with hours in stock, price per hour, scenarios and which requirements they meet, plus custom-collection options when nothing in stock fits.

Esquema de entrada

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "What data is needed: domain, task, volume, resolution, frame rate, session length, sensors."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}
🟢get_dataset(slug)

Full details for one DataClue dataset: overview, scenarios with measured sample specs, delivered resolution, sensors, annotation, delivery format, licensing, FAQs, sample clip URLs and price.

Esquema de entrada

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Dataset slug, e.g. \"manufacturing-operations\", \"household-activities\", \"warehouse-logistics\"."
    }
  },
  "required": [
    "slug"
  ],
  "additionalProperties": false
}
🟢get_pricing_and_terms

DataClue's list prices, what is included, quote paths (free samples, paid pilot, volume quote, custom collection), licensing, payment and consent terms.

Esquema de entrada

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🔴request_quote(email, name, company, plan, datasets, ...)

Send a request to DataClue's sales team on the user's behalf: free evaluation samples, a paid pilot, a volume quote, or a custom collection. Side effect: emails the request (including the user's name, work email and company) to DataClue, who reply to that email. Only call this when the user has asked to contact DataClue and has provided their own work email, and show them the details to confirm before sending. Each call sends a new request, so do not retry a successful call.

Esquema de entrada

{
  "type": "object",
  "properties": {
    "email": {
      "type": "string",
      "description": "The user's work email. DataClue replies here."
    },
    "name": {
      "type": "string",
      "description": "The user's name (required except for samples)."
    },
    "company": {
      "type": "string",
      "description": "The user's company (required except for samples)."
    },
    "plan": {
      "type": "string",
      "enum": [
        "sample",
        "pilot",
        "volume",
        "custom"
      ],
      "description": "sample = free evaluation clips; pilot = 10-hour paid pilot; volume = quote for a number of hours; custom = data collected to order."
    },
    "datasets": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Dataset slugs (from search_datasets). Required for pilot and volume."
    },
    "scenarios": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Optional scenario slugs."
    },
    "hours": {
      "type": "number",
      "description": "Hours needed. Required for volume."
    },
    "use_case": {
      "type": "string",
      "enum": [
        "Robot policy / VLA pretraining",
        "World model training",
        "Perception & action recognition",
        "Evaluation / benchmarking",
        "Research (non-commercial)",
        "Other"
      ],
      "description": "Intended use. Required for volume."
    },
    "timeline": {
      "type": "string",
      "description": "When the data is needed."
    },
    "details": {
      "type": "string",
      "description": "Requirements in the user's words. Required for custom (what should be collected)."
    }
  },
  "required": [
    "email",
    "plan"
  ],
  "additionalProperties": false
}

Comunidad

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Evidencia

Observaciones recientes

verificadoversión no registrada4 herramientas