DataClue — AI training data

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

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
83%
Qualität der Benennung
95%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~832Tokens (Tool-Definitionen)
~1.3 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (0.65% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

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

Remote-Endpunkte

https://dataclue.app/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (4)

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🟢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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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
}

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