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Timestamped web intelligence for AI agents. MCP server with guaranteed freshness envelopes.

9 starsTypeScript

MCP server that timestamps every web extraction for AI agents

by Prince-Gabriel·Mar 3, 2026·1 point·0 comments

AI Analysis

●●●BangerBig BrainSolve My ProblemZero to One

Timestamps every web extraction for agents—solves real AI hallucination failure mode.

Strengths
  • Addresses a genuine, overlooked problem: LLMs can't distinguish recency without metadata envelopes.
  • Ships 7 tools plus composite extract_landscape for unified competitive intelligence queries.
  • Works locally (Playwright) and serverless (Cloudflare Workers), practical deployment flexibility.
Weaknesses
  • Reliance on web scraping means fragility when sites change—no resistance testing shown.
  • Scraper-based approach doesn't scale to real-time data or private/authenticated sources.
Target Audience

AI/LLM developers, agents, researchers

Similar To

Tavily AI · Firecrawl · Browserless

Post Description

LLMs hallucinate recency. They'll tell you a project is active when it hasn't been touched in two years, or cite outdated docs as current. The problem isn't the content — it's that there's no timestamp on when it was retrieved.

FreshContext wraps every extraction in an envelope with retrieved_at, content_date, and freshness_confidence fields. The agent always knows when it's looking at data, not just what it says.

Ships with 7 tools: GitHub repo extraction, Hacker News sentiment, Google Scholar, YC startup scraper, GitHub repo search, npm/PyPI package trends, and extract_landscape — one call that queries all sources simultaneously and returns a unified competitive intelligence report.

Works locally (Playwright) and on Cloudflare Workers (Browser Rendering API). MIT license.

Would love feedback on the FreshContext envelope pattern specifically — is this a useful primitive for agent reliability?

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1012d ago