brainiall-image

endpoint

https://api.brainiall.com/mcp/image/mcp

protocol

http-sse ·2025-06-18

authentication

bearer

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is brainiall-image live?

Yes — it answered the hub's last check (checked 2h ago). It answered 99% of checks over the last 30 days.

Is brainiall-image free to use?

Partly — some of its tools are open, others need a key or payment.

What tools does brainiall-image have?

10 tools: run_skillsets, document_tables, document_extract, remove_background, upscale_image, document_to_markdown, restore_face, document_query, ….

Is brainiall-image safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• check_image_service reads open 19h ago

  Check health status of Image API services and loaded models. Returns: dict with keys: - status (str): 'healthy' or error state - models (dict): Loaded model status per capability - version (str): API version

  mcp-tool

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  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• understand_content reads auth-required 19h ago

  Multimodal extraction. Send an image, text, or both; define your schema of fields; get structured JSON. Brainiall Content Understanding engine. Unified multimodal field extraction over images and text.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "text": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Optional pre-extracted text"
      },
      "image": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Optional base64 image (will OCR first)"
      },
      "field_schema": {
        "anyOf": [
          {
            "type": "object",
            "additionalProperties": true
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Map of field_name -> description, e.g. {\"invoice_id\":\"invoice number\",\"total\":\"amount due\"}"
      }
    }
  }

  arguments 42 lines copy
• run_skillsets reads unknown never probed

  Run a multi-skill enrichment pipeline over a document image or text in one call. Brainiall Skillsets engine. Returns per-skill outputs ready for indexing or RAG.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "text": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Pre-extracted text (skip OCR)"
      },
      "image": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Base64 image (triggers OCR)"
      },
      "skills": {
        "anyOf": [
          {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Enrichment skills: ocr | entities | language | keyphrases | sentiment"
      }
    }
  }

  arguments 44 lines copy
• document_tables reads unknown 19h ago

  Reconstruct every table in a document image into headers and rows. Returns: dict with keys: table_count (int), tables (list of {title, headers, rows, row_count, column_count}); [] if there are no tables.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "image"
    ],
    "properties": {
      "image": {
        "type": "string",
        "description": "Base64-encoded PNG/JPEG of the document page"
      }
    }
  }

  arguments 12 lines copy
• document_extract reads unknown 2h ago

  Turn a document image into structured fields. doc_type picks the schema (receipt/invoice/id/contract/form/generic). A page with no readable text returns an error rather than a guess. Returns: dict with keys: doc_type (str), fields (dict — null for any value not present), text (str — the recognised plain text).

  mcp-tool

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  {
    "type": "object",
    "required": [
      "image"
    ],
    "properties": {
      "image": {
        "type": "string",
        "description": "Base64-encoded PNG/JPEG of a single document page"
      },
      "doc_type": {
        "type": "string",
        "default": "generic",
        "description": "The document kind — picks the field schema: receipt | invoice | id | contract | form | generic | business_card | w2 | health_card | mortgage | pay_stub"
      }
    }
  }

  arguments 17 lines copy
• remove_background reads unknown never probed

  Remove the background from an image. Uses Brainiall Cutout engine segmentation to precisely separate foreground from background. Returns a base64-encoded image with transparent background (PNG) or white background (WebP). Sub-500ms latency on GPU. Args: image_base64: Base64-encoded image data (PNG, JPEG, or WebP). output_format: Output format -- 'png' (with transparency) or 'webp'. Returns: dict with keys: - image_base64 (str): Base64-encoded result image - format (str): Output image format - original_size (dict): Original width and height - processing_ms (int): Processing time in milliseconds

  mcp-tool

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  {
    "type": "object",
    "required": [
      "image_base64"
    ],
    "properties": {
      "image_base64": {
        "type": "string",
        "maxLength": 20000000,
        "description": "Base64-encoded image data. Supports PNG, JPEG, and WebP formats."
      },
      "output_format": {
        "type": "string",
        "default": "png",
        "description": "Output image format: 'png' (default, with transparency) or 'webp'"
      }
    }
  }

  arguments 18 lines copy
• upscale_image reads unknown 11h ago

  Upscale image resolution with the Brainiall image-upscaling engine. Enhances image resolution by 2x or 4x with the GPU-accelerated Brainiall image-upscaling engine super-resolution. Processes in tiles (256x256) to manage VRAM. Maximum output dimension: 8192x8192. Args: image_base64: Base64-encoded image data (PNG, JPEG, or WebP). scale: Upscale factor -- 2 or 4 (default: 4). Returns: dict with keys: - image (str): Base64-encoded upscaled image - format (str): Output image format - width (int): Output width - height (int): Output height - scale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds

  mcp-tool

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  {
    "type": "object",
    "required": [
      "image_base64"
    ],
    "properties": {
      "scale": {
        "type": "integer",
        "default": 4,
        "description": "Upscale factor: 2 or 4 (default: 4)"
      },
      "image_base64": {
        "type": "string",
        "maxLength": 20000000,
        "description": "Base64-encoded image data. Supports PNG, JPEG, and WebP formats."
      }
    }
  }

  arguments 18 lines copy
• document_to_markdown reads unknown 11h ago

  Return the document as structured Markdown (headings, tables, lists, code blocks, math). Brainiall Doc Layout engine. The single API for converting documents to LLM-friendly format.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "pdf"
    ],
    "properties": {
      "pdf": {
        "type": "string",
        "description": "Base64-encoded PDF document"
      },
      "page_range": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ],
        "default": null,
        "description": "Page range like '1,2,5-10' or null for all pages"
      }
    }
  }

  arguments 24 lines copy
• restore_face reads unknown 11h ago

  Restore and enhance faces in an image with the Brainiall face-restoration engine. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background with the Brainiall image-upscaling engine. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with the Brainiall image-upscaling engine (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds

  mcp-tool

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  {
    "type": "object",
    "required": [
      "image_base64"
    ],
    "properties": {
      "upscale": {
        "type": "integer",
        "default": 2,
        "description": "Output upscale factor: 1-4 (default: 2)"
      },
      "image_base64": {
        "type": "string",
        "maxLength": 20000000,
        "description": "Base64-encoded image data containing one or more faces."
      },
      "enhance_background": {
        "type": "boolean",
        "default": true,
        "description": "Enhance background with the Brainiall image-upscaling engine (default: true)"
      }
    }
  }

  arguments 23 lines copy
• document_query reads unknown 2h ago

  Ask a natural-language question about a document image; returns a grounded answer plus the supporting line. Returns found:false rather than guessing when the document doesn't contain the answer. Returns: dict with keys: answer (str|null), found (bool), supporting_text (str|null), text (str).

  mcp-tool

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  {
    "type": "object",
    "required": [
      "image",
      "question"
    ],
    "properties": {
      "image": {
        "type": "string",
        "description": "Base64-encoded PNG/JPEG of the document page"
      },
      "question": {
        "type": "string",
        "maxLength": 1000,
        "description": "The natural-language question about the document"
      }
    }
  }

  arguments 18 lines copy

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