Loading ToolBench
Model Context Protocol based AI development assistant with 36 specialized tools for TypeScript, JavaScript, and Python projects - Now with Tasks support, cursor-based Pagination, 9-step reasoning framework and Gemini prompting strategies
Hi-AI presents 36 tools with mixed definition quality. Strengths: all tools have basic names starting with action verbs, most descriptions present. Critical weaknesses: (1) Parameter descriptions are often generic or missing entirely, many tools list params without explaining what they control or accept. (2) No visible input schema validation or constraints (enums, ranges, patterns) in most tools, LLMs will hallucinate invalid values. (3) Output schemas are not documented, agents cannot predict response structure for chaining. (4) Error handling descriptions are absent, LLMs have no recovery guidance. (5) Tool design shows composition issues: memory tools (save_memory, recall_memory, update_memory, delete_memory, etc.) cluster 6 similar operations without clear differentiation or guidance on when to use each. (6) Thinking tools (create_thinking_chain, analyze_problem, step_by_step_analysis, break_down_problem, think_aloud_process) are largely duplicative, overlap in purpose reduces clarity. (7) Code quality tools (validate_code_quality, analyze_complexity, suggest_improvements) lack concrete scope documentation. Average tool definition quality is below the production baseline of 194 chars for descriptions and lacks the precision seen in A-grade servers.
Analyze code complexity metrics
Analyze a problem to understand its components and requirements
Analyze a prompt for effectiveness
Analyze and break down requirements
Apply quality rules and standards
Apply 9-step reasoning framework to analyze complex problems systematically
Automatically save the current context to memory
Break down a complex problem into smaller, manageable sub-problems
Parameter descriptions are generic or missing concrete guidance. Tools list parameter names and types but omit constraints (enums, ranges, patterns), valid values, and usage context. LLMs cannot reliably infer what values are acceptable and will hallucinate invalid options.
Output schemas are not documented in tool definitions. Agents cannot predict response structure, fields, or types. This breaks tool chaining, agents cannot know what data to extract for downstream calls.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-21 | F | 48 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 34 | 1.25.1+ | v1 |
Check code coupling and cohesion metrics
Create a chain of thinking for step-by-step problem solving
Create user stories for features
Delete a memory from long-term storage
Enhance a prompt for better LLM responses
Enhance a prompt using Gemini-specific prompting strategies
Generate a feature roadmap
Find all references to a symbol in the codebase
Find a symbol definition in TypeScript/JavaScript code
Format analysis results as an actionable plan
Generate a Product Requirements Document
Get coding conventions and best practices guide
Get the current date and time
Inspect and analyze network requests from a browser session
List all stored memories with optional filtering
Monitor and analyze console logs from a browser session
Preview UI components as ASCII art
Set priority for a memory item
Recall information from long-term memory by key
Restore a previously saved session context
Save information to long-term memory
Search through all stored memories
Start a new memory session with context
Perform a detailed step-by-step analysis of a problem
Suggest code improvements based on quality standards
Guide through a thinking-aloud process for problem solving
Update the value of an existing memory
Validate code against quality standards
Error handling descriptions are absent. Tools have no documented error cases, recovery guidance, or what to do on failure. LLMs will have no context for retrying or reporting errors to users.
Thinking tools are duplicative and lack clear differentiation. create_thinking_chain, analyze_problem, step_by_step_analysis, break_down_problem, and think_aloud_process all operate on problem decomposition but lack guidance on which to choose for specific scenarios.
Memory tools cluster 6 similar operations (save, recall, update, delete, list, search) without clear differentiation or composition guidance. When should an agent use search_memories vs list_memories? What does recall_memory return that list_memories does not?
Enum constraints are not declared for parameters accepting a known set of values. 'format' param in format_as_plan accepts 'outline, checklist, gantt, roadmap' but is defined as free-form string, not enum. LLMs will hallucinate other values.
Parameter descriptions lack format guidance. 'steps' param in create_thinking_chain says 'Number of thinking steps (default: 5)' but does not state valid range (min/max). 'priority' in save_memory says '0-10' in description but not as a constraint in schema.
No visible input validation or sanitization. Tools accept path parameters (project_path, url) without documented constraints against path traversal or injection. LLMs could be tricked into passing malicious payloads.
Tools marked WRITE (save_memory, update_memory, auto_save_context, prioritize_memory, start_session) and DESTRUCTIVE (delete_memory) lack confirmation or dry-run patterns. Agents could accidentally delete critical memories without recourse.
Code quality tools lack scope clarity. 'validate_code_quality' accepts 'rules' as array of strings but does not enumerate valid rule names or document what each rule checks. 'analyze_complexity' accepts 'metrics' without listing available metrics.
Planning tools (generate_prd, create_user_stories, analyze_requirements, feature_roadmap) lack guidance on dependencies. generate_prd lists 'user_personas' as optional but does not state impact on output if omitted. create_user_stories requires 'feature' but unclear if it needs prior analysis.