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AI dataset 'ingredients label' analyzer - reverse-engineer datasets, estimate costs, analyze quality, and generate production workflows
DataRecipe MCP server has 10 tools with complete input schemas and descriptions, but quality is inconsistent. Naming follows verb_noun convention (parse_, generate_, analyze_, extract_, compare_, profile_, get_, enhance_). Descriptions range 50-180 chars, meeting baseline minimums. However, parameter descriptions are sparse or generic ('Path to...', 'HuggingFace dataset ID'), lacking actionable constraints. Output schemas are undocumented, LLMs cannot predict return structure. Error handling is absent from all tool definitions. No tool annotations (readOnlyHint/destructiveHint) despite clear risk profiles (WRITE vs READ_ONLY). Tool composition is reasonable but some tools conflate concerns (e.g., generate_spec_output both generates artifacts AND estimates costs). Missing idempotency guidance and recovery paths.
Run deep analysis on a HuggingFace dataset and generate reproduction guide.
Compare multiple HuggingFace datasets side by side. Returns comparison metrics and recommendations.
Apply LLM-enhanced context to regenerate analysis reports with rich, dataset-specific content. Use after analyze_huggingface_dataset returns an enhancement_prompt. Process the prompt yourself and pass the resulting JSON here to replace template placeholders with tailored analysis.
Extract system prompt templates from a HuggingFace dataset. Returns unique prompts categorized by domain.
Extract scoring rubrics and evaluation patterns from a HuggingFace dataset. Returns structured templates for annotation guidelines.
Generate project artifacts (annotation spec, executive summary, milestone plan, cost breakdown) from analysis JSON.
No output schemas documented. LLMs cannot predict return structure (fields, types, pagination). Violates pattern:tool and pattern:response-shaper. All 10 tools lack return type documentation.
No tool annotations (readOnlyHint/destructiveHint). Tools marked READ_ONLY or WRITE in metadata but not in schema. LLMs cannot determine safety/idempotency without explicit hints.
Parameter descriptions lack actionable constraints. 'Path to the document file' does not specify format, length, or valid extensions. 'HuggingFace dataset ID' does not explain format (e.g., 'org/dataset-name'). Violates pattern:constrained-input.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 64 | 2026-07-28+ | v2 |
Get the AI Agent context file from a previous analysis. Returns structured data for AI Agent consumption.
Get the LLM extraction prompt template for analyzing a specification document. Use this when you want to analyze a document yourself instead of using an external API.
Parse a specification document (PDF, Word, image, text) and extract text content. Returns the document text and a prompt for LLM analysis.
Generate annotator profile and cost estimation for a dataset. Returns required skills, team size, and budget.
No error handling guidance. Tools lack recovery hints (e.g., 'If dataset not found, try search_datasets()'). No categorization of retryable vs fatal errors. Violates pattern:recovery-guide.
generate_spec_output and enhance_analysis_reports accept complex nested JSON objects (analysis_json, enhancement_json) without schema validation hints. LLMs cannot infer required fields or structure. Violates pattern:tool-description.