Loading ToolBench
A Model Context Protocol server with Exa for web search, academic paper search, and more. Uses Nango for secure authentication and provides 8 different search tools including web search, research papers, company research, and social media search.
Tools have basic descriptions and parameter schemas, but lack depth and LLM-optimization. Most descriptions are under 100 chars and generic (e.g., 'Search Wikipedia articles' for wikipedia_search_exa). Parameter descriptions are minimal or absent. No output schemas documented. No error handling guidance. Tool names follow verb_noun pattern but descriptions don't explain WHEN to use each tool or what distinguishes them. No tool annotations (readOnlyHint, etc.). Schemas present but sparse, many parameters lack descriptions. This is typical D-grade community server territory.
Research companies using Exa AI - finds comprehensive information about businesses, organizations, and corporations. Provides insights into company operations, news, financial information, and industry analysis. Uses Nango for authentication.
Find competitors for a business using Exa AI - identifies similar companies, competitive landscape analysis, and market positioning. Helps discover direct and indirect competitors in any industry. Uses Nango for authentication.
Extract and crawl content from specific URLs using Exa AI - retrieves full text content, metadata, and structured information from web pages. Ideal for extracting detailed content from known URLs. Uses Nango for authentication.
Search GitHub repositories and code using Exa AI - finds repositories, code snippets, documentation, and developer profiles on GitHub. Useful for finding open source projects, code examples, and technical resources. Uses Nango for authentication.
Search LinkedIn profiles and companies using Exa AI - finds professional profiles, company pages, and business-related content on LinkedIn. Useful for networking, recruitment, and business research. Uses Nango for authentication.
Output schemas not documented. Tools return results but LLMs cannot predict field structure, forcing them to guess at downstream tool parameters. Breaks tool chaining.
Parameter descriptions missing or minimal. 'numResults' has description but 'query' lacks context on format/length. 'searchType' enum in linkedin_search_exa and github_search_exa lacks explanation of what each option returns.
Tool descriptions too generic and short. 'Search Wikipedia articles' (24 chars) tells LLM nothing about when to use it vs web_search_exa. No WHEN/WHY guidance. Baseline is 194 chars average for A+ tools.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 47 | <=2025-11-25 | v2 |
Search for academic papers and research using Exa AI - specializes in finding scholarly articles, research papers, and academic content. Returns detailed information about research findings and academic sources. Uses Nango for authentication.
Real-time web search using Exa AI
Search Wikipedia articles
No error handling guidance. Tools will fail (invalid API key, rate limit, network error) but descriptions don't explain recovery steps. LLM has no actionable next step on failure.
No tool annotations. All tools are read-only but lack readOnlyHint annotation. Prevents clients from optimizing caching/retry logic. Missing idempotentHint for search tools.