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Secure, governed RAG template with role-based access control using Mastra
This server has significant definition quality gaps across all dimensions. Tool descriptions are present but generic and lack LLM-optimization. Input schemas are provided with some structure but lack consistent type definitions and descriptions for parameters. Output schemas are undocumented. No error handling guidance. Only one tool (arxiv-pdf-parser) meets minimal acceptable description length; others range from 74-167 chars but lack actionable context. Parameter descriptions are sparse or missing detail. No documented output structures for downstream chaining. The server provides readable schemas but lacks the specificity required for production-grade agent interaction.
Access cryptocurrency market data from Alpha Vantage including crypto prices, exchange rates, and historical data
Access stock market data from Alpha Vantage including stock prices, quotes, technical indicators, and fundamental data
Search and retrieve academic papers from arXiv.org including metadata, abstracts, and download links
Download and parse arXiv PDF papers to markdown format with lazy-loaded pdf-parse
Analyze competitive positioning, market gaps, and differentiation strategies
Check compliance policies, legal contracts, and regulatory requirements (Enterprise only)
Generic, non-actionable tool descriptions. E.g., 'Access cryptocurrency market data from Alpha Vantage including crypto prices, exchange rates, and historical data' does not explain WHEN to use this tool vs alternatives, WHAT the LLM should expect in the response, or HOW to invoke it correctly. Descriptions lack LLM-optimization (under 194 chars baseline but also lack specificity).
Parameter descriptions are missing or insufficiently detailed. E.g., 'function' parameter in alpha-vantage-stock has description 'Stock-specific Alpha Vantage API function' but does not explain WHAT EACH FUNCTION DOES or WHEN TO USE EACH. E.g., TIME_SERIES_INTRADAY vs TIME_SERIES_DAILY distinction is not explained. LLMs cannot distinguish between options without this guidance.
Inferred effective spec: 2025-06-18+.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-21 | F | 42 | 2025-06-18+ | v2 |
| 2026-03-09 | F | 48 | - | v1 |
Output schemas completely undocumented. Tools return structured data but LLMs have no visibility into what fields to expect, required for downstream tool chaining and data extraction. No documented return types violates 100% baseline for A+ tools.
Error handling is not documented. Tools provide no guidance on what errors can occur, whether they are retryable, or what the LLM should do next. E.g., what if an arXiv ID is invalid? What if Alpha Vantage API rate limit is exceeded? No recovery guidance.
Naming clarity issues: 'competitive-intelligence' and 'compliance-check' are verb-noun but vague. What ACTION does competitive-intelligence perform? Analyze? Compare? Summarize? LLMs cannot infer precise intent without action verbs. Should be 'analyze_competitive_position', 'search_competitors', or 'compare_competitor_features'.
Optional parameters lack clear defaults and mutual exclusivity documentation. E.g., arxiv tool has 'query', 'id', 'author', 'title' all optional with no guidance on which to provide or what happens if multiple are given. LLMs will guess and likely pass invalid combinations.
No pagination limits documented for list-returning tools. arxiv accepts 'max_results' (1-100) but competitive-intelligence and compliance-check 'topK' lack explicit bounds. No guidance on default behavior when result sets are large, risking context window exhaustion.
Tool composition risk: competitive-intelligence and compliance-check are single-purpose 'analyze' tools with no clear upstream context. No documented relationship to search/lookup tools. If LLM needs to analyze competitors, what tools exist to fetch competitor data? Chaining is unclear.