Loading ToolBench
A Rust-based agent runtime system with plugin architecture supporting MCP (Model Context Protocol) servers, tools, skills, memory, and LLM integrations
This server has 27 tools with basic schemas and descriptions, but significant quality gaps. Most tool descriptions are under 100 chars (below the 194-char baseline for A+ tools). Parameter descriptions are minimal or absent. No output schemas documented. Error handling is not visible in the code samples. Tool naming is inconsistent: some follow verb_noun (read, write, grep), others are nouns (bash, python, agent). The 'agent' and 'task_*' tools lack detail on return structures. No evidence of input validation, rate limiting, or permission gates. Schemas are present but sparse, most parameters lack type constraints, enums, or range limits. The server appears to be a custom Rust runtime without standard MCP transport visibility.
Spawn a sub-agent to handle a complex sub-task independently. Use when the task can be decomposed into parallel or isolated work. The sub-agent has independent message history and returns a compacted result. Sub-agents cannot spawn further sub-agents.
Ask the user for clarification or additional information.
Execute shell commands
Edit file contents with line-based operations
Check if a file or directory exists
Report that the task cannot be completed.
Provide the final answer to the user when the task is complete.
Find files by name or other criteria
Descriptions are uniformly short (20-60 chars), well below the 194-char baseline for A+ tools. Most lack context on WHEN to use the tool, prerequisites, or return structure. E.g., 'Execute shell commands' does not explain timeout behavior, working directory semantics, or error recovery.
Parameter descriptions are minimal or absent. 'operations' in edit tool lacks detail on format (line numbers? diff syntax? JSON?). 'timeout_ms' in bash is a string type but should be integer with min/max bounds. No enum constraints on status values in task_create/task_update.
No output schemas documented. Tools like agent, task_create, web_search return unspecified structures. LLMs cannot plan downstream calls or extract required fields (e.g., task_id from task_create). This violates the response-shaper pattern.
Inferred effective spec: 2025-06-18+.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 42 | 2025-06-18+ | v2 |
Find files matching a glob pattern
Search for text patterns in files
Get file or directory metadata
List directory contents
Execute Python code
Read file contents
Install a skill from a source URL
Inspect table structure and metadata
Preview table data
Get table schema information
List sheets in a table file
Create a new task for tracking progress. Returns the task ID.
Get details of a specific task by ID.
List all tracked tasks with their status.
Update an existing task's status and/or output.
Search for and load a deferred tool's schema. Available deferred tools can be requested on demand.
Fetch content from a URL
Search the web for information
Write content to a file
Destructive tools (bash, write, edit, python, skill_install, agent) lack confirmation or dry-run support. No evidence of permission gates or audit logging. An agent could delete all files without safeguards.
No error handling guidance visible. Tools do not document retryable vs. fatal errors, or provide actionable recovery hints. E.g., if bash times out, should the agent retry with a longer timeout, or fail the task?