Cloud Vision API MCP Server

# generated by fastapi-codegen: # filename: openapi.yaml # timestamp: 2025-06-29T03:06:15+00:00 import argparse import json import os from typing import * from typing import Optional from autogen.mcp.mcp_proxy import MCPProxy from autogen.mcp.mcp_proxy.security import BaseSecurity, UnsuportedSecurityStub from fastapi import Query from models import ( Alt, FieldXgafv, GoogleCloudVisionV1p1beta1AsyncBatchAnnotateFilesRequest, GoogleCloudVisionV1p1beta1AsyncBatchAnnotateImagesRequest, GoogleCloudVisionV1p1beta1BatchAnnotateFilesRequest, GoogleCloudVisionV1p1beta1BatchAnnotateFilesResponse, GoogleCloudVisionV1p1beta1BatchAnnotateImagesRequest, GoogleCloudVisionV1p1beta1BatchAnnotateImagesResponse, Operation, ) app = MCPProxy( contact={'name': 'Google', 'url': 'https://google.com', 'x-twitter': 'youtube'}, description='Integrates Google Vision features, including image labeling, face, logo, and landmark detection, optical character recognition (OCR), and detection of explicit content, into applications.', license={ 'name': 'Creative Commons Attribution 3.0', 'url': 'http://creativecommons.org/licenses/by/3.0/', }, termsOfService='https://developers.google.com/terms/', title='Cloud Vision API', version='v1p1beta1', servers=[{'url': 'https://vision.googleapis.com/'}], ) @app.post( '/v1p1beta1/files:annotate', description=""" Service that performs image detection and annotation for a batch of files. Now only "application/pdf", "image/tiff" and "image/gif" are supported. This service will extract at most 5 (customers can specify which 5 in AnnotateFileRequest.pages) frames (gif) or pages (pdf or tiff) from each file provided and perform detection and annotation for each image extracted. """, tags=[ 'vision_file_annotation', 'vision_image_annotation', 'vision_project_file_annotation', 'vision_project_image_annotation', ], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_files_annotate( field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1BatchAnnotateFilesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") @app.post( '/v1p1beta1/files:asyncBatchAnnotate', description=""" Run asynchronous image detection and annotation for a list of generic files, such as PDF files, which may contain multiple pages and multiple images per page. Progress and results can be retrieved through the `google.longrunning.Operations` interface. `Operation.metadata` contains `OperationMetadata` (metadata). `Operation.response` contains `AsyncBatchAnnotateFilesResponse` (results). """, tags=['vision_file_annotation', 'vision_project_file_annotation'], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_files_async_batch_annotate( field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1AsyncBatchAnnotateFilesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") @app.post( '/v1p1beta1/images:annotate', description=""" Run image detection and annotation for a batch of images. """, tags=['vision_image_annotation', 'vision_project_image_annotation'], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_images_annotate( field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1BatchAnnotateImagesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") @app.post( '/v1p1beta1/images:asyncBatchAnnotate', description=""" Run asynchronous image detection and annotation for a list of images. Progress and results can be retrieved through the `google.longrunning.Operations` interface. `Operation.metadata` contains `OperationMetadata` (metadata). `Operation.response` contains `AsyncBatchAnnotateImagesResponse` (results). This service will write image annotation outputs to json files in customer GCS bucket, each json file containing BatchAnnotateImagesResponse proto. """, tags=[ 'vision_image_annotation', 'vision_file_annotation', 'vision_project_image_annotation', 'vision_project_file_annotation', ], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_images_async_batch_annotate( field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1AsyncBatchAnnotateImagesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") @app.post( '/v1p1beta1/{parent}/files:annotate', description=""" Service that performs image detection and annotation for a batch of files. Now only "application/pdf", "image/tiff" and "image/gif" are supported. This service will extract at most 5 (customers can specify which 5 in AnnotateFileRequest.pages) frames (gif) or pages (pdf or tiff) from each file provided and perform detection and annotation for each image extracted. """, tags=[ 'vision_file_annotation', 'vision_image_annotation', 'vision_project_file_annotation', 'vision_project_image_annotation', ], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_projects_locations_files_annotate( parent: str, field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1BatchAnnotateFilesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") @app.post( '/v1p1beta1/{parent}/files:asyncBatchAnnotate', description=""" Run asynchronous image detection and annotation for a list of generic files, such as PDF files, which may contain multiple pages and multiple images per page. Progress and results can be retrieved through the `google.longrunning.Operations` interface. `Operation.metadata` contains `OperationMetadata` (metadata). `Operation.response` contains `AsyncBatchAnnotateFilesResponse` (results). """, tags=[ 'vision_file_annotation', 'vision_image_annotation', 'vision_project_file_annotation', 'vision_project_image_annotation', ], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_projects_locations_files_async_batch_annotate( parent: str, field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1AsyncBatchAnnotateFilesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") @app.post( '/v1p1beta1/{parent}/images:annotate', description=""" Run image detection and annotation for a batch of images. """, tags=['vision_image_annotation', 'vision_project_image_annotation'], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_projects_locations_images_annotate( parent: str, field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1BatchAnnotateImagesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") @app.post( '/v1p1beta1/{parent}/images:asyncBatchAnnotate', description=""" Run asynchronous image detection and annotation for a list of images. Progress and results can be retrieved through the `google.longrunning.Operations` interface. `Operation.metadata` contains `OperationMetadata` (metadata). `Operation.response` contains `AsyncBatchAnnotateImagesResponse` (results). This service will write image annotation outputs to json files in customer GCS bucket, each json file containing BatchAnnotateImagesResponse proto. """, tags=['vision_image_annotation', 'vision_project_image_annotation'], security=[ UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), UnsuportedSecurityStub(name="None"), ], ) def vision_projects_locations_images_async_batch_annotate( parent: str, field__xgafv: Optional[FieldXgafv] = Query(None, alias='$.xgafv'), access_token: Optional[str] = None, alt: Optional[Alt] = None, callback: Optional[str] = None, fields: Optional[str] = None, key: Optional[str] = None, oauth_token: Optional[str] = None, pretty_print: Optional[bool] = Query(None, alias='prettyPrint'), quota_user: Optional[str] = Query(None, alias='quotaUser'), upload_protocol: Optional[str] = None, upload_type: Optional[str] = Query(None, alias='uploadType'), body: GoogleCloudVisionV1p1beta1AsyncBatchAnnotateImagesRequest = None, ): raise RuntimeError("Should be patched by MCPProxy and never executed") if __name__ == "__main__": parser = argparse.ArgumentParser(description="MCP Server") parser.add_argument( "transport", choices=["stdio", "sse", "streamable-http"], help="Transport mode (stdio, sse or streamable-http)", ) args = parser.parse_args() if "CONFIG_PATH" in os.environ: config_path = os.environ["CONFIG_PATH"] app.load_configuration(config_path) if "CONFIG" in os.environ: config = os.environ["CONFIG"] app.load_configuration_from_string(config) if "SECURITY" in os.environ: security_params = BaseSecurity.parse_security_parameters_from_env( os.environ, ) app.set_security_params(security_params) mcp_settings = json.loads(os.environ.get("MCP_SETTINGS", "{}")) app.get_mcp(**mcp_settings).run(transport=args.transport)