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DINO-X Image Detection MCP Server

detect-all-objects

Analyze images to detect, count, and locate all objects with detailed descriptions using the DINO-X Image Detection MCP Server.

Instructions

Analyze an image to detect all identifiable objects, returning the category, count, coordinate positions and detailed descriptions for each object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageFileUriYesURI of the input image. Preferred for remote or local files. Must start with 'https://' or 'file://'.
includeDescriptionYesWhether to return a description of the objects detected in the image, but will take longer to process.

Implementation Reference

  • Core handler implementation in DinoXApiClient that performs the actual API call to DINO-X service for detecting all objects using a universal prompt.
    async detectAllObjects( imageFileUri: string, includeDescription: boolean ): Promise<DetectionResult> { return this.performDetection(imageFileUri, includeDescription, { model: "DINO-X-1.0", prompt: { type: "universal", universal: 1 }, targets: ["bbox"], bbox_threshold: 0.25, iou_threshold: 0.8 }); }
  • Registers the 'detect-all-objects' tool in the STDIO MCP server, including input schema (Zod) and execution handler that calls the DinoXApiClient.
    private registerDetectAllObjectsTool(): void { const { name, description } = ToolConfigs[Tool.DETECT_ALL_OBJECTS]; this.server.tool( name, description, { imageFileUri: z.string().describe("URI of the input image. Preferred for remote or local files. Must start with 'https://' or 'file://'."), includeDescription: z.boolean().describe("Whether to return a description of the objects detected in the image, but will take longer to process."), }, async (args) => { try { const { imageFileUri, includeDescription } = args; if (!imageFileUri) { return { content: [ { type: 'text', text: 'Image file URI is required', }, ], } } const { objects } = await this.api.detectAllObjects(imageFileUri, includeDescription); const categories: ResultCategory = {}; for (const object of objects) { if (!categories[object.category]) { categories[object.category] = []; } categories[object.category].push(object); } const objectsInfo = objects.map(obj => { const bbox = parseBbox(obj.bbox); return { name: obj.category, bbox, ...(includeDescription ? { description: obj.caption, } : {}), } }); return { content: [ { type: "text", text: `Objects detected in image: ${Object.keys(categories).map(cat => `${cat} (${categories[cat].length})` )?.join(', ')}.` }, { type: "text", text: `Detailed object detection results: ${JSON.stringify(objectsInfo, null, 2)}` }, { type: "text", text: `Note: The bbox coordinates are in [xmin, ymin, xmax, ymax] format, where the origin (0,0) is at the top-left corner of the image. These coordinates help determine the exact position and spatial relationships of objects in the image.` }, ] }; } catch (error) { return { content: [ { type: 'text', text: `Failed to detect objects from image: ${error instanceof Error ? error.message : String(error)}`, }, ], }; } } ) }
  • Registers the 'detect-all-objects' tool in the HTTP MCP server, including input schema (Zod) and execution handler that calls the DinoXApiClient.
    private registerDetectAllObjectsTool(): void { const { name, description } = ToolConfigs[Tool.DETECT_ALL_OBJECTS]; this.server.tool( name, description, { imageFileUri: z.string().describe("URI of the input image. Preferred for remote or local files. Must start with 'https://'."), includeDescription: z.boolean().describe("Whether to return a description of the objects detected in the image, but will take longer to process."), }, async (args) => { try { const { imageFileUri, includeDescription } = args; if (!imageFileUri) { return { content: [ { type: 'text', text: 'Image file URI is required', }, ], } } const { objects } = await this.api.detectAllObjects(imageFileUri, includeDescription); const categories: ResultCategory = {}; for (const object of objects) { if (!categories[object.category]) { categories[object.category] = []; } categories[object.category].push(object); } const objectsInfo = objects.map(obj => { const bbox = parseBbox(obj.bbox); return { name: obj.category, bbox, ...(includeDescription ? { description: obj.caption, } : {}), } }); return { content: [ { type: "text", text: `Objects detected in image: ${Object.keys(categories).map(cat => `${cat} (${categories[cat].length})` )?.join(', ')}.` }, { type: "text", text: `Detailed object detection results: ${JSON.stringify(objectsInfo, null, 2)}` }, { type: "text", text: `Note: The bbox coordinates are in [xmin, ymin, xmax, ymax] format, where the origin (0,0) is at the top-left corner of the image. These coordinates help determine the exact position and spatial relationships of objects in the image.` }, ] }; } catch (error) { return { content: [ { type: 'text', text: `Failed to detect objects from image: ${error instanceof Error ? error.message : String(error)}`, }, ], }; } } )
  • Tool schema definition: name and description used across registrations.
    [Tool.DETECT_ALL_OBJECTS]: { name: Tool.DETECT_ALL_OBJECTS, description: "Analyze an image to detect all identifiable objects, returning the category, count, coordinate positions and detailed descriptions for each object.", },
  • Helper utility to parse bounding box array [xmin,ymin,xmax,ymax] into named object, used in tool response formatting.
    export const parseBbox = (bbox: number[]) => { return { xmin: parseFloat(bbox[0].toFixed(1)), ymin: parseFloat(bbox[1].toFixed(1)), xmax: parseFloat(bbox[2].toFixed(1)), ymax: parseFloat(bbox[3].toFixed(1)) }; };

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