hzy9981
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DASHSCOPE_API_KEY | Yes | Your DashScope API key | |
| GOOGLE_CLOUD_PROJECT | Yes | Your Google Cloud project ID | |
| GOOGLE_APPLICATION_CREDENTIALS_JSON | No | Your Google Cloud project ID |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| read_promptC | Get the prompt content with given prompt id. |
| create_promptC | Create a prompt with given content, system instruction, model and display name. |
| update_promptC | Update a prompt with given prompt_id and new content, system instruction, model, and display name. |
| delete_promptC | Delete a prompt with given prompt_id. |
| list_promptsC | Lists Vertex prompts matching a given display name. |
| write_data_driven_optimize_configA | Constructs a JSON configuration for Data-Driven Optimize and uploads it to GCS. This tool generates a JSON configuration file based on the provided parameters and uploads it to the specified Google Cloud Storage URI. This configuration file is required to run data-driven prompt optimization. Args:
gcs_config_uri: The GCS URI where the generated VAPO config JSON file will
be saved (e.g., 'gs://my-bucket/vapo/config.json').
prompt_optimizer_method: The method for prompt optimization. Either
'VAPO' or 'OPTIMIZATION_TARGET_GEMINI_NANO'.
target_model_endpoint_url: The custom endpoint URL for the target model.
Required for Gemini Nano target.
base_config: Optional. A dictionary representing the base configuration.
modifications: Optional. A dictionary representing the modifications to
apply to the base config.
base_config_path: Optional. Path to a base config file. If provided and
Returns: A string containing a success message and details about the uploaded configuration file, including a link to the Vertex AI console. |
| run_data_driven_optimizeB | Starts a data-driven prompt optimization job on Vertex AI. This method uses a dataset and configurable metrics. The Args: config_gcs_path: The Google Cloud Storage URI (e.g., "gs://your-bucket/config.json") to a JSON file containing the Prompt Optimizer configuration. This is required. service_account: The service account email to run the job. This is required. prompt_optimizer_method: The method for prompt optimization. Either 'VAPO' or 'OPTIMIZATION_TARGET_GEMINI_NANO'. wait_for_completion: If True, the tool will block until the Vertex AI CustomJob completes. Defaults to False. Returns: A string indicating the status and details of the optimization job, including a link to the Vertex AI console. |
| run_few_shot_optimizationA | Applies few shot prompt optimization to a prompt using user provided dataset and method. Args: prompt_to_optimize: The zero-based index of the prompt to improve. example_path: GCS path to the csv file containg few-shot examples method: The optimization method to use for few shot prompt improvement. The method should be one of the following: - TARGET_RESPONSE: Optimize the prompt to match the target response. - RUBRICS: Optimize the prompt to improve the rubrics scores. Returns: Optimized prompt. |
| analyze_data_driven_optimize_resultsC | Analyzes results and saves the detailed data to files. |
| generate_html_reportC | Generates a comprehensive HTML report from Data-Driven Optimize analysis results. |
| get_token_usage_statsA | 获取 MCP 服务的 token 使用统计信息。 |
| call_dashscope_mcpC | 调用 DashScope 的远程 MCP 服务并获取结果。 Args: tool_name: DashScope MCP 中的工具名称 arguments: 传递给工具的参数字典 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 12 tools
Most tools have distinct purposes: prompt CRUD, data-driven optimization workflow, and few-shot optimization. However, 'call_dashscope_mcp' is a generic MCP caller that overlaps with the optimization tools' intent, and 'get_token_usage_stats' is unrelated, causing minor ambiguity.
Prompt tools follow a consistent CRUD pattern (create/delete/list/read/update_prompt). Optimization tools mix styles: 'run_data_driven_optimize' and 'run_few_shot_optimization' are consistent, but 'analyze_data_driven_optimize_results', 'write_data_driven_optimize_config', and 'generate_html_report' are long and vary in structure. 'call_dashscope_mcp' uses a different verb_noun pattern.
With 12 tools, the set covers two main domains (prompt management and prompt optimization) without being overwhelming. Each tool contributes to a clear workflow, and the count is appropriate for the scope.
Prompt management has full CRUD coverage. The optimization workflow includes setup, execution, analysis, and report generation. Missing features like job cancellation or monitoring are covered by links to the Vertex AI console. The addition of generic utilities ('call_dashscope_mcp', 'get_token_usage_stats') does not detract from domain completeness.