Loading ToolBench
A multi-agent orchestration system using Google ADK and A2A SDK with story writing and image generation capabilities
This server exhibits fundamental gaps in tool definition quality. While three tools are present with basic descriptions, they lack comprehensive parameter descriptions, proper input schemas, and meaningful output documentation. The tools appear to be part of a Google ADK/A2A framework integration rather than a pure MCP server. Tool definitions are inferred from function signatures rather than explicitly registered with full schemas. No evidence of error handling patterns, recovery guidance, or structured output specifications. Parameter descriptions are minimal or absent. The `exit_loop` tool has an empty input schema with no documentation. `load_image_data` has a single parameter with a description but no output schema documented. `image_generator_tool` likewise lacks output documentation.
Exits the refinement loop by setting escalate flag to true
Generates an image using the Vertex AI Imagen model
Downloads an image from a Google Cloud Storage URI and saves it as an artifact
exit_loop tool has empty input schema {} with no parameter definitions or constraints. Tools with no input schemas must score 0 on schema dimension.
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract relevant data without knowing return structure.
image_generator_tool description is only 44 characters ('Generates an image using the Vertex AI Imagen model'), below effective minimum of 50-100 chars. Lacks WHEN to use, WHAT it returns, and any prerequisites.
exit_loop tool has description of 66 characters but no actionable context for when to invoke it or what state changes occur beyond setting escalate flag. LLM cannot reason about consequences.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 40 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 34 | - | v1 |
No error handling patterns visible. load_image_data returns status/error_message dict, but no guidance on retry-ability, recovery steps, or actionable remediation for common failures (invalid GCS URI, auth failure, blob not found).
image_generator_tool accepts a 'prompt' parameter with description 'The prompt describing the image to generate' but no constraints on length, format, tone, or guardrails. LLM can pass arbitrary text including unsafe/harmful prompts.
load_image_data tool accepts 'image_uri' parameter but does not document valid format, protocol requirements (must start with gs://), or what happens if the GCS bucket/blob is not found. Code shows validation but description omits it.
Tools appear to be registered via Google ADK framework rather than explicit MCP tool registration. Tool definitions are inferred from function signatures (load_image_data signature, image_generator_tool signature) rather than visible in an explicit MCP tools array.