Loading ToolBench
A Python-based AI agent framework with tool execution, memory persistence, skill loading, and approval workflows. Supports multiple LLM providers (OpenAI, Anthropic) and implements the ReAct pattern for agentic reasoning.
This DIY agent toolkit provides 6 tools with complete JSON Schema definitions and basic descriptions. However, several tools lack important context about when to use them, what they return, and how they compose. Naming is verb-forward and mostly clear (read_file, edit_file, search_files, exec_command, remember, memory_search), following the pattern baseline. Parameter descriptions exist but are minimal, many lack constraints, format hints, or usage guidance. Output schemas are not documented: the code shows truncation and return types (strings, JSON) but does not formally describe what fields clients should expect. Error handling is minimal, handlers return truncated strings or generic success messages without actionable recovery guidance. The `exec_command` tool is particularly risky: it accepts arbitrary shell commands with a 30-second timeout but lacks permission gates, dry-run options, or destructive operation warnings. The memory tools (`remember`, `memory_search`) have basic schemas but lack documentation of the memory_store behavior, scope behavior, or what metadata fields are allowed. File tools (read_file, edit_file, search_files) accept arbitrary paths with no validation or path-traversal guards visible in the excerpted code. Composition is reasonable, tools chain naturally (search_files → read_file → edit_file), but response payloads are not designed to include chaining IDs or structured results.
Replace a 1-based inclusive line range in a text file. Read the file first, then edit only the needed lines.
Execute a shell command and return stdout, stderr, and exit code.
Search persistent memory with case-insensitive keyword matching.
Read numbered lines from a text file. Use start_line/end_line to continue reading long files.
Persist an important user preference, project fact, or durable note.
Search for text in files under a directory.
No output schema documentation. Tools return truncated strings, JSON blobs, or unstructured text. LLMs cannot reliably parse results or chain downstream calls. For example, `memory_search` returns 'No memories found' or a JSON dump; clients don't know the JSON structure (fields, types). `read_file` returns numbered lines but no formal schema for structured consumption.
`exec_command` is destructive and dangerous without permission gates or dry-run. The tool accepts arbitrary shell commands (e.g., `rm -rf /`). There is no evidence of permission checks, confirmation steps, or destructive operation warnings. The description does not indicate this is a write/destructive operation. Agents could accidentally execute harmful commands.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 54 | 2026-07-28+ | v2 |
Parameter descriptions are minimal and lack constraint documentation. For example, `search_files` glob parameter has description 'File glob pattern. Defaults to **/*.' but does not explain syntax, recursion behavior, or limits. `edit_file` replacement parameter lacks context about line ending handling, encoding, or whether multi-line replacements are supported. `exec_command` timeout is '30 seconds' but no guidance on retry, backoff, or what happens on timeout.
No error classification or recovery guidance. Tools return generic messages ('OK: remembered ...', 'No memories found') or rely on exception strings. There is no guidance for LLMs on whether errors are retryable, user-fixable, or fatal. For example, if `read_file` hits a permission error or encoding issue, how should the agent respond? No visibility into failure modes.
File operations lack input validation and path-traversal guards. The `read_file`, `edit_file`, and `search_files` tools accept arbitrary paths. There is no visible validation to prevent path traversal attacks (e.g., `../../etc/passwd`), symlink attacks, or access to sensitive directories. Agents could be tricked into reading or modifying system files.
Memory tools lack scope and metadata documentation. `remember` accepts scope and metadata parameters, but the description does not define valid scope values, metadata schema, or persistence guarantees. `memory_search` returns results but does not document the structure of returned memory records (fields, types, order).
No idempotency or duplicate-prevention markers. Tools like `remember` (write operation) and `edit_file` (modify operation) do not declare idempotency. Agents retrying on ambiguous failures could create duplicate memories or apply edits twice. No guidance for agents on whether repeating a call is safe.
Tool composition is weak. Response payloads do not include IDs or references for chaining. For example, `memory_search` returns memory content but no explicit memory_id; calling `remember` again requires re-querying. `read_file` returns line numbers but no file_id or next_read_cursor in structured form. Agents must infer chaining parameters from unstructured responses.