Loading ToolBench
A multi-purpose Model Context Protocol server providing utility tools for AI assistants
MCP Swiss Knife provides 10 tools with consistent Zod-based schema definitions and reasonably detailed descriptions. Strengths: all tools have explicit schemas with proper typing, descriptions are substantive (avg 180 chars), parameter validation is present via Zod. Weaknesses: no parameter-level descriptions in most tools (only property types visible in JSON Schema), no documented output schemas, no error handling guidance, missing tool annotations (readOnlyHint/destructiveHint/idempotentHint), parameter constraints are minimal (only basic type constraints and format validation), and descriptions lack 'when to use this instead of similar tools' differentiation. Tool composition is sound, each tool has a single clear responsibility, but the definition presentation lacks LLM-optimization details that would help an agent select tools confidently. Parameter descriptions are largely absent; LLMs must infer meaning from bare parameter names like 'query', 'url', 'paths', 'content' without guidance on format, constraints, or expected values.
Performs web search using Gemini AI with WebSearch capability. Returns comprehensive search results with AI-powered analysis and summarization.
Get the current date and time in ISO 8601 format.
Converts web page content to well-formatted Markdown, preserving structural elements like tables and definition lists. Recommended as the default tool for web content extraction when a clean, readable text format is needed while maintaining document structure.
Extracts and converts the main content area of a web page to Markdown format, automatically removing navigation menus, headers, footers, and other peripheral content. Perfect for capturing the core content of articles, blog posts, or documentation pages.
Retrieves raw text content directly from a URL without browser rendering. Ideal for structured data formats like JSON, XML, CSV, TSV, or plain text files. Best used when fast, direct access to the source content is needed without processing dynamic elements.
Missing parameter-level descriptions. Parameters like 'query' (in gemini_web_search, search_notes), 'url' (in all web-fetch tools), 'paths' (read_notes), and 'language' (get_software_documentation_prompt) have no descriptions in the generated JSON Schema. The rubric requires every parameter to have a description explaining what it controls and expected format/constraints. LLMs cannot infer meaning from bare parameter names.
No documented output/response schemas. Tools define inputs but do not document what fields are returned or their types. For example, read_notes, search_notes, and all web-fetch tools lack explicit return type documentation. LLMs need to know expected output structure to plan downstream calls and extract relevant data.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 68 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 48 | - | v1 |
Fetches fully rendered HTML content using a headless browser, including JavaScript-generated content. Essential for modern web applications, single-page applications (SPAs), or any content that requires client-side rendering to be complete.
Get a prompt for creating software project design documents. Returns instructions for generating product.md, structure.md, and tech.md files.
Reads the contents of multiple notes from your Obsidian vault. Each note's content is returned with its path as a reference. The "paths" argument must be an array of strings, representing the relative paths of notes in the vault. Failed reads for individual notes won't stop the entire operation. Reading too many notes at once may result in an error. Example: { "paths": ["Daily/2025-08-23.md", "Projects/Idea.md"] }
Searches for notes in your Obsidian vault by name. The search is case-insensitive and supports partial matches or valid regular expressions. Returns an array of note paths that match the query. Example: { "query": "meeting" }
Writes content to an Obsidian note. If the note at the specified path does not exist, it will be created. If it exists, its content will be overwritten. The "path" argument must be a string representing the relative path of the note in the vault (e.g., "Daily/2025-08-23.md"). The "content" argument must be a string containing the full content to write to the note. Example: { "path": "Daily/2025-08-23.md", "content": "# Daily Log\n- Meeting with team\n- Work on project X" }
No tool annotations for operation semantics. Tools like write_note (WRITE risk) and destructive operations lack destructiveHint annotation. Tools like get_current_datetime and read operations lack readOnlyHint annotation. These annotations help agents reason about side effects and safety without reading full descriptions.
Minimal parameter constraints and validation guidance. Most parameters accept free-form strings without enums, ranges, or patterns. For example, 'query' parameters lack guidance on minimum/maximum length, case sensitivity, or regex support. The write_note 'path' parameter lacks clarification on path traversal restrictions. Parameter constraints should be explicit and documented.
No error handling or recovery guidance. Tools do not document error conditions, what can go wrong, or what the agent should do if a call fails. For example, write_note could fail if the vault directory is invalid or permissions are denied, but there is no guidance on recovery. read_notes states 'Failed reads for individual notes won't stop the entire operation' but does not specify the error response format.
Web-fetch tools (get_raw_text, get_rendered_html, get_markdown, get_markdown_summary) lack guidance on distinguishing when to use each. Descriptions explain capabilities but do not provide clear decision criteria (e.g., 'use get_raw_text for JSON APIs', 'use get_markdown for articles', 'use get_markdown_summary for blog posts'). Agents may choose incorrectly without this guidance.
Missing pagination parameters. Tools like read_notes and search_notes that could return many results lack limit/offset or cursor parameters. Description for read_notes states 'Reading too many notes at once may result in an error' but provides no way for agents to control batch size or paginate.