imageSearchText
Search for images in a dataset using text queries to find matching visual content based on natural language descriptions.
Instructions
根据输入的文本内容，从数据集中检索出与输入的文本内容相符的图片
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | 检索的文本 |
Input Schema (JSON Schema)
{
"$schema": "http://json-schema.org/draft-07/schema#",
"additionalProperties": false,
"properties": {
"text": {
"description": "检索的文本",
"type": "string"
}
},
"required": [
"text"
],
"type": "object"
}
Implementation Reference
- Core handler function implementing the imageSearchText tool logic using Tencent Cloud CI API for text-based image search.async imageSearchText(params: ImageSearchTextParams) { // 验证并解析参数 const validParams = ImageSearchTextParamsSchema.parse(params); const { text } = validParams; try { const key = 'datasetquery/imagesearch'; // 固定值 const appid = this.bucket.split('-').pop(); const host = `${appid}.ci.${this.region}.myqcloud.com`; const url = `https://${host}/${key}`; const body = JSON.stringify({ DatasetName: this.datasetName, Mode: 'text', Text: text, }); const result = await this.cos.request({ Method: 'POST', // 固定值，必须 Key: key, // 必须 Url: url, // 请求的url，必须 Body: body, // 请求体参数，必须 Headers: { // 设置请求体为 json，固定值，必须 'Content-Type': 'application/json', // 设置响应体为json，固定值，必须 Accept: 'application/json', }, }); return { isSuccess: true, message: '图像检索成功', data: result, }; } catch (error) { return { isSuccess: false, message: '请求异常: ${error.message}', data: error, }; } }
- Zod schema and TypeScript type definition for input parameters of the imageSearchText tool.export const ImageSearchTextParamsSchema = z.object({ text: z.string(), }); export type ImageSearchTextParams = z.infer<typeof ImageSearchTextParamsSchema>;
- src/server.ts:511-529 (registration)MCP server registration of the 'imageSearchText' tool, including inline input schema and delegation to the service handler.server.tool( 'imageSearchText', '根据输入的文本内容，从数据集中检索出与输入的文本内容相符的图片', { text: z.string().describe('检索的文本'), }, async ({ text }) => { const res = await CIMateInsightInstance.imageSearchText({ text }); return { content: [ { type: 'text', text: JSON.stringify(res.data, null, 2), }, ], isError: !res.isSuccess, }; }, );