Update to OfferHunt

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README.md →

plugin.json →

README.md

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-# OfferHunt 0.3.4
+# OfferHunt 0.3.5
 
 Job data and tools for personal AI agents. This package exposes sixteen MCP tools through https://www.offerhunt.ai/mcp with OAuth authorization. There is no embedded UI. Conversations may use the user's language; resume parsing currently accepts English PDF/DOCX/text. Ask for the original PDF/DOCX file (a host file reference, or save_resume with no input for a 24-hour upload link); text supports matching but not applications.
 
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 Agents own scheduling, browser application forms and authorized mailbox reading. OfferHunt returns current jobs, confirmed preferences, private resume URLs and durable application records. An automatic submission requires claim_application before filling the form and a fresh prepare_application_submission check right before submitting. Only confirmed outcomes are recorded as applied with track_application; mark_application_uncertain holds unclear outcomes for verification, mark_application_blocked records obstacles, and release_application_claim frees an attempt only after the user confirms nothing was submitted.
 
-Recommendations wait for answered preferences: when get_recommendations returns needs_preferences, ask its missing_preferences questions together, offer its resume-based suggestions for confirmation, and save the answers with update_preferences (no_preference for "anything is fine"). Users can instead answer on their account page (answer_on_website); it writes the same record, so read get_profile rather than asking again.
+Recommendations wait for answered preferences: when get_recommendations returns needs_preferences, ask its missing_preferences questions together, offer its resume-based suggestions for confirmation, and save the answers with update_preferences (no_preference for "anything is fine"). Users can instead answer on their account page (answer_on_website); it writes the same record, so read get_profile rather than asking again. get_recommendations returns the user's matches a page at a time; the first page holds only strong matches. When more_matches is above zero, say how many more there are and call it with next_cursor when the user asks; later matches score lower. If a cursor returns list_changed, start again without one.
 
 For daily match-only tasks, confirm the user's time and time zone before creating a schedule and report its next run only after host confirmation. Call get_recommendations directly for each run; it reads current matching context, checks pause state, and returns the preferences used. A separate get_profile or application-record read is not required for match-only runs. Application workflows still require fresh profile and application records.