Loading ToolBench
A web-based workbench for managing and interacting with MCP servers, LLM providers, and chat sessions with support for file attachments, audio transcription, templates, and multiple LLM backends.
MCP Workbench has 19 tools with reasonable naming and mostly complete descriptions. Tool names follow verb_noun conventions (upload_attachment, get_attachments, delete_attachment, etc.). Most tools have descriptions in the 50-200 character range, appropriate for LLM context. However, input parameter schemas are incomplete: while parameter names and descriptions are present, JSON Schema type information is missing from the provided source. Output schemas are undocumented. Error handling is mentioned as a feature but no evidence of recovery guidance or actionable error messages in the source code. Tool composition is reasonable, tools are single-purpose (create_chat vs update_chat vs delete_chat), but output chaining fields are not documented. Security considerations for credential handling are not visible in the provided code.
Send chat messages to LLM providers with support for vision models, vision attachments, system prompts, MCP tools, and multiple backends (OpenAI, Anthropic, Google, Groq, Ollama, LMStudio, OpenRouter, Together, Mistral, Cohere).
Create a new chat session with optional title, system prompt, default provider, and tool server configuration.
Create a new message in a chat with support for user/assistant/tool/system roles, attachments, tool calls, tool results, and token counting.
Create a new chat template with category, description, system prompt, and public/private visibility.
Delete an attachment by ID from both filesystem and database.
Delete a chat and all its associated messages and attachments.
Delete a chat template by ID.
Input parameter schemas lack explicit JSON Schema type definitions. While parameter names and descriptions are provided, type information (e.g., 'type': 'string', 'type': 'number') is missing or incomplete in the source. This prevents LLMs from validating input constraints and reduces schema usability.
Output schemas are not documented for any tool. The source code shows tool descriptions and input parameters but provides no visibility into return value structure, field types, or pagination details. This forces LLMs to infer output structure, risking misinterpretation.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 55 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 35 | - | v1 |
Export a chat conversation as CSV format with columns for message index, role, provider, model, content, tool calls, and token counts.
Export a chat conversation as JSON format including chat metadata and all messages with tool calls and results.
Retrieve all attachments for a specific chat message by message ID.
Retrieve a specific chat with all its messages, attachments, and metadata.
Retrieve a specific template by ID.
List chat templates with optional filtering by category, search query, and public/private status. Supports pagination and popular templates query.
Increment the usage counter for a template when it is used.
List all chats with summary information including title, creation date, message count, and last message preview.
Transcribe audio files to text using various LLM providers (OpenAI Whisper, Groq, HuggingFace, Replicate). Supports multiple audio formats with optional language specification and context prompt.
Update chat properties including title, system prompt, default provider/model, and tool server associations.
Update an existing chat template.
Upload file attachments for chat messages. Validates file type and size, stores file in filesystem and database, supports images (JPEG, PNG, GIF, WebP), PDF, and text formats.
No evidence of error handling guidance or recovery suggestions in tool definitions. Tools like delete_chat (destructive), delete_attachment, and delete_template should document error conditions (permission denied, not found, in use) and actionable recovery steps (e.g., 'Try list_chats() to find the correct ID').
Destructive tools (delete_chat, delete_attachment, delete_template) and potentially dangerous operations (chat_completion with external LLM providers) lack confirmation or dry-run semantics. No evidence of a dry-run parameter or explicit confirmation requirement before irreversible actions.
Tools accepting arbitrary LLM provider and model parameters (transcribe_audio, chat_completion, create_chat) should validate enums for provider/model to prevent hallucinated values. transcribe_audio documents provider enum correctly, but chat_completion's provider parameter lacks enum constraint documentation.
No visible security context or permission declarations. Tools handling sensitive data (chat contents, transcriptions, credentials for external LLM APIs) should document required permissions (e.g., 'read:chats', 'write:chats') and credential injection strategy.
Tool increment_template_usage is vaguely named. The action is unclear from the name alone, does it increment a counter? Reset usage? Update metrics? Rename to a clearer verb like 'record_template_usage' or 'track_template_usage'.
Pagination and result limiting not explicitly documented. Tools returning lists (get_templates, list_chats, get_chat with messages) should document result limits, pagination support (offset/limit or cursor), and total count fields to prevent context window exhaustion.