Loading ToolBench
A flexible framework for building Model Context Protocol servers with dynamic tool loading, supporting multiple transports (stdio, SSE, streamable-http). Includes example implementations for filesystem access, chromadb vector search, text-to-speech, and mindmap generation.
This MCP server framework exhibits significant quality gaps across definition standards. Tool definitions are present but lack essential depth. Of 8 tools listed, there are actually only 5 unique tools (read_file, list_directory, get_cwd, convert_markdown_to_html, retrieve_documents appear once or twice each). Naming uses appropriate verb-noun patterns (read_, list_, get_, convert_, retrieve_), meeting basic verb conventions. However, descriptions are generic and lack the specificity required for LLM tool selection. Input schemas exist but parameter descriptions are minimal and do not explain constraints, formats, or valid ranges. The markdown_to_html and retrieve_documents tools have slightly better descriptions, but most tools fall well below the 194-char baseline for production tools. Error handling is absent, there is no guidance for recovery, no distinction between retryable vs fatal errors, and no handling for edge cases like file-not-found or invalid paths. No output schemas are documented, meaning LLMs cannot plan downstream chaining. Security is a major concern: file operations (read_file, list_directory) have no path traversal protection visible, and file operation descriptions do not mention permission checks or sanitization. The framework dynamically loads tools from Python modules but provides no validation of tool definitions at load time.
Converts a markdown string into an interactive HTML mindmap using markmap.js.
Return the current working directory.
Return the current working directory.
List contents of a directory
List contents of a directory
Read and return the contents of a file.
Read and return the contents of a file.
File operation tools (read_file, list_directory) lack path traversal protection documentation and no input validation is visible. No mention of permission checks, symlink handling, or allowed directory restrictions.
No output schemas documented for any tool. LLMs cannot predict return structure, preventing tool chaining and forcing manual parsing of responses.
Tool descriptions are too generic and under the 194-char baseline. 'Read and return the contents of a file' does not explain when to use it, prerequisites, limitations (file size, encoding), or what happens on errors.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 48 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 47 | - | v1 |
Retrieves documents from the global Chroma vector store based on a query.
No error handling guidance. Tools return raw exceptions as TextContent with no recovery hints. E.g., 'File not found' should suggest 'Check the path or use list_directory() to explore available files.'
Parameter descriptions are minimal. 'filepath' described only as 'The file path to read', no format constraints, encoding details, max file size, or character restrictions documented.
Duplicate tool definitions in the source listing (read_file, list_directory, get_cwd appear twice). This indicates either schema documentation errors or actual duplication in the framework.
convert_markdown_to_html output_filename parameter is optional but described as 'Optional' in name only; no default behavior documented. Does it auto-generate a name? Fail if not provided? Ambiguous for LLM.
retrieve_documents 'k' parameter is integer but no bounds stated. Can LLM pass k=0, k=1000000? Unbounded integers invite API overload.