Loading ToolBench
An AI-powered i18n automation toolset for React/Vue projects.
Server has 14 tools with explicit schemas and descriptions. Naming follows verb_noun pattern (check_status, scan_strings, audit_missing, etc.). Descriptions are present but vary in quality, some are concise and action-oriented (e.g., 'Check the current i18n status'), others are vague or lack context (e.g., 'Refine an existing proposal based on human feedback' lacks WHEN/WHY guidance). Parameter descriptions are present for most tools but inconsistent in detail. Input schemas use proper JSON Schema with types. Output schemas are NOT documented in the source, LLMs cannot predict return structure. Error handling is minimal; no recovery guidance visible. Security: path validation present (_validate_safe_path), but no permission gates or audit logging visible. Composition is reasonable, tools are single-purpose and chainable (scan → audit → propose → commit). Baseline: avg tool description ~120 chars (within 10 - 1024 range), but lacks LLM-optimized structure (WHAT/WHEN/WHY). No tool annotations (readOnlyHint/destructiveHint) despite clear risk levels (READ_ONLY vs WRITE).
[Validation Phase] Find keys present in base language but missing in target language.
[Expert Audit] Generate a full quality report with typography and variable safety checks.
Check the current i18n status of the project. Returns: Enabled languages, source directories, locales directory, and environment health.
[Cleanup Phase] Report-only dead-key detection for locale files. This tool does not mutate locale files.
[Commit Phase] Formally apply pending proposals to the physical locale files.
Extract project metadata to help the AI understand the business domain and tone.
Output schemas not documented. LLMs cannot predict return structure or plan downstream tool calls. Tools like propose_sync, optimize_targets, and audit_quality lack documented return types.
Tool annotations missing. Despite explicit Risk levels (READ_ONLY vs WRITE), no readOnlyHint or destructiveHint annotations present. Agents cannot distinguish safe from destructive operations.
Descriptions lack WHEN/WHY context. Many descriptions state WHAT but not WHEN to call or WHY vs similar tools. E.g., 'Refine an existing proposal based on human feedback' does not explain when to call vs propose_sync.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 65 | <=2025-11-25 | v2 |
[Learning Loop] Detect manual modifications in locale files and promote them to APPROVED status.
[Optimization Phase] Export unpolished or non-approved entries for batch AI optimization.
[Semantic Alignment] Use a familiar language as a reference to optimize a target language.
[Sync Phase] Generate a translation synchronization proposal.
Refine an existing proposal based on human feedback.
Save a confirmed term translation into local GLOSSARY.json for future reuse.
Save confirmed business persona (domain, audience, tone) to configuration.
[Extraction Phase] Extract hardcoded strings from source code.
No error recovery guidance. Error handling is minimal; no actionable error messages or recovery paths visible. E.g., if propose_sync fails due to invalid lang_code, LLM has no guidance on next steps.
Parameter constraints not fully specified. lang_code accepts 'all' as a literal string in some tools but not others; no enum or pattern constraint visible. Invites hallucinated values.