Local file access and system command execution proxy for Chrome extension. Provides file read/write, system command execution, and agent collaboration features.
This server exposes 7 tools with significant definition gaps. Tool names lack clear action verbs (agent_file, agent_trash, agent_exec, agent_search, agent_skill, manage_agent, exec_log). Descriptions exist but are terse (10-50 chars for most), leaving LLM intent unclear. Input schemas are present with enums and basic types, but parameter descriptions are minimal or missing context. No output schemas documented. Error handling is absent, no recovery guidance, no categorization of retryable vs fatal errors. The server implements dangerous operations (shell exec, file write, trash recovery) without confirmation patterns or permission gates. Security concerns: no mention of credential injection, no audit logging visible, no rate limiting.
Execute a shell command on the agent machine. Returns stdout/stderr and exit code
File operations on the agent local filesystem. read: get file content; write: create/overwrite file (requires content); list: list directory; delete: move to trash; download: download file to browser
Search files or file content on the agent machine. file: find files by name pattern; content: search text/regex inside files
Load or run a Skill. run: execute a Workflow Skill (requires params); load: load an Agent Skill instruction into context
Manage agent trash bin. list: view deleted items; restore: recover from trash (requires trashId)
Extract historical execution logs to analyze successful paths and failure lessons.
Tool names lack action verbs. 'agent_file', 'agent_trash', 'agent_exec' do not clearly signal what action occurs. Should be 'read_file', 'write_file', 'delete_file', 'restore_from_trash', 'execute_command'.
Descriptions are too brief (10-50 chars). LLMs cannot determine when to select these tools vs alternatives. E.g., 'Manage agent trash bin' does not explain when to call restore vs list, or what 'trash' means in this context.
No output schemas documented. LLMs cannot plan downstream calls or extract required fields. E.g., agent_search returns results but structure is unknown; agent_file read action returns content but format is undocumented.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 45 | 2026-07-28+ | v2 |
List or switch paired AI agents
Destructive operations (agent_exec, agent_file write/delete, agent_skill run) lack confirmation or dry-run patterns. An LLM can execute arbitrary shell commands or delete files without safeguards.
No error handling guidance. Tools do not return actionable error messages (e.g., 'File not found. Try search_files() first'). LLMs cannot recover from failures or know if errors are retryable.