Loading ToolBench
MCP server for Google Gemini CLI integration providing AI-powered code review, bug analysis, feature planning, and code explanation tools
This server demonstrates MODERATE definition quality with reasonable structure but significant gaps in depth and consistency. All 4 tools are explicitly registered with Pydantic models and descriptions. However, descriptions are generic and lack LLM-specific guidance; parameters lack enum constraints where appropriate; output schemas are partially documented but not comprehensive; error handling is minimal. The server follows basic MCP patterns but falls short of production-grade quality expected for complex AI-assisted tools.
Analyze bugs and suggest fixes using Gemini. Provides root cause analysis, fix suggestions, and implementation guidance.
Explain code functionality, logic, and purpose using Gemini. Provides detailed explanations at varying levels of detail with support for specific questions.
Review and improve feature plans and specifications using Gemini. Analyzes feature plans for completeness, clarity, technical feasibility, and provides suggestions for improvement.
Analyze code quality, style, and potential issues using Gemini. Provides comprehensive code review including bug detection, security analysis, performance optimization suggestions, and best practice recommendations.
Vague and under-specified descriptions. Tool descriptions lack actionable context for LLM selection. Example: 'Provides comprehensive code review including bug detection...' is generic; does not explain WHEN to use this vs alternatives, what conditions make it most useful, or when output is unreliable. Descriptions average ~140 chars but lack specificity.
Missing enum constraints on parameters that should be bounded. 'focus' parameter in gemini_review_code accepts string but should be an enum (security|performance|style|bugs|general). 'detail_level' in gemini_explain_code should be enum (basic|intermediate|advanced). Free-form strings invite hallucinated values; enums prevent invalid LLM submissions.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-21 | F | 49 | 2026-07-28+ | v2 |
| 2026-03-09 | D | 53 | - | v1 |
Incomplete parameter descriptions. Several parameters lack clarity on format/constraints: 'focus' in gemini_review_code has no explicit statement that only 5 values are valid; 'context' parameter defaults to empty string but description does not explain what happens if omitted; 'questions' parameter in gemini_explain_code is vague on scope (single question? multiple? markdown-formatted?). LLMs cannot infer parameter semantics from names alone.
Output schemas documented in Pydantic models but not integrated into tool descriptions. LLMs see the tool description (brief and generic) but must infer output structure from response context. No tool explicitly documents what fields to expect, in what order, or which fields are always present vs conditional. Response models exist (CodeReviewResponse, GeminiToolResponse) but are not referenced in tool docstrings.
Minimal error handling and no recovery guidance. Code sample shows try-catch structure but captured exception handling does not provide actionable guidance. If template loading fails ('Code review template not found'), the LLM receives only a ValueError. No suggestion for the LLM to retry, check configuration, or fall back to a generic review. Error responses should teach the agent how to recover.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). All four tools are explicitly marked Risk: READ_ONLY in the summary but no @property or schema attribute in the Pydantic definitions to signal this to clients. Tool annotations are a current MCP pattern (2026-07-28 spec) that allow clients to optimize execution (e.g., avoid retrying idempotent operations unnecessarily, or warn before destructive actions).
Duplicate and inconsistent response models. gemini_review_code uses CodeReviewResponse with fields (summary, issues, suggestions, rating, input_prompt, gemini_response); other tools use generic GeminiToolResponse (result, input_prompt, gemini_response, metadata). Inconsistent response structures force LLMs to handle multiple formats for logically similar operations, increasing error likelihood.
Parameters accept nullable string fields without clear behavior documentation. 'language' parameter defaults to None in three tools; description says 'Programming language' but does not explain what happens if omitted (auto-detect? error? generic analysis?). Undocumented optional parameters force LLMs to guess intent.
No dependency hints in descriptions. gemini_analyze_bug accepts optional 'code_context', 'error_logs', 'environment', and 'reproduction_steps' but does not explain which combinations are most useful, or if minimal parameters suffice for basic analysis. LLMs must reason about which fields to populate, wasting tokens and risking incomplete submissions.