A Recursive Language Model (RLM) server that analyzes massive text/code documents by breaking them into chunks and recursively querying sub-LLMs to find patterns and answer complex questions without exceeding token limits.
Single tool with critical definition gaps. Tool name 'analyze_massive_document' is somewhat descriptive but lacks action verb clarity. Description is in Indonesian and very brief (82 chars), below the 194-char baseline. Input schema present but lacks per-parameter descriptions, parameters 'file_path' and 'query' have minimal context. Output is undocumented free-text string with no schema. No error categorization or recovery guidance. No input validation constraints (file size limits, format specs). The implementation shows basic error handling (file not found, empty file, exception catch) but responses are unstructured and non-actionable for LLM self-correction. Security concern: tool reads arbitrary files from disk without validation or sandboxing, potential path traversal risk.
Tools untuk menganalisis dokumen teks/kode yang SANGAT BESAR.
Tool description is non-English (Indonesian) and under 100 characters, insufficient for LLM selection logic
Input parameters 'file_path' and 'query' lack individual descriptions explaining expected format, constraints, or examples
Output schema completely undocumented, returns unstructured string with no declared fields or format
Error responses are not actionable for LLM recovery. 'Error: File tidak ditemukan' does not guide next steps or suggest alternatives
No input validation constraints: no file size limits, no format validation, no timeout specification despite internal LLM calls
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 37 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 28 | - | v1 |
Path traversal vulnerability: tool accepts arbitrary file_path without validation, could allow reading /etc/passwd, private keys, or other sensitive files
API keys for LLM backends hardcoded in config.yaml and loaded via environment variables; if config or .env is readable by attacker, credentials are exposed
No timeout on LLM calls or code execution loop, agent could hang indefinitely on large files or malformed config
exec() in RLMSession is a code injection risk if user_query or file_content contain malicious Python; sandboxing is minimal (__builtins__ set to empty dict but builtins like open are available via module imports)