Li Data Scraper MCP Server
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TDQS
Scored across 20 tools
There is significant overlap between tools targeting similar resources, such as get_profile_post_and_comments, get_profile_post_comment, and get_profiles_comments, which could confuse an agent about which to use for profile comments. However, descriptions help differentiate some tools, like distinguishing company vs. profile operations, preventing complete ambiguity.
Naming is inconsistent with mixed patterns: most tools use get_* or search_* prefixes, but about_the_profile deviates, and some names are overly verbose or include escaped characters (e.g., get_profile_data_and_connection_u0026_follower_count). This lack of a uniform convention reduces predictability and readability.
With 20 tools, the count is borderline high for a data scraper server, feeling slightly heavy but not extreme. It covers multiple domains (profiles, companies, posts, searches), which justifies some volume, but could benefit from consolidation to avoid overlap and improve coherence.
The tool set provides broad coverage for LinkedIn data scraping, including profile, company, post, comment, reaction, and search operations, with minor gaps such as missing update or delete tools (expected for read-only scraping) and no direct tool for managing credits or errors. Overall, it supports core workflows without major dead ends.