The freshcontext-mcp project provides timestamped web intelligence specifically designed for AI agents, facilitating a deeper understanding of both the content and the timeline of the information they process. By utilizing the FreshContext envelope, this innovative server ensures that every piece of data is accompanied by key metadata, empowering AI systems to distinguish when data was retrieved from what the data contains.

The Challenge

AI models often misrepresent information recency, leading to potential inaccuracies such as referencing outdated job postings or stale API documentation as current. Current systems fail to provide a reliable timestamp, resulting in instances where agents can present outdated information with the same confidence as recent insights. The lack of temporal context creates ambiguity and undermines the integrity of the information retrieved.

FreshContext Envelope Solution

To combat these issues, freshcontext-mcp wraps each data result within a structured envelope that includes:

[FRESHCONTEXT]
Source: https://github.com/owner/repo
Published: 2024-11-03
Retrieved: 2026-03-03T10:14:00Z
Confidence: high
---
... content ...
[/FRESHCONTEXT]

This ensures that AI agents can reliably determine not only the content they are working with but also its relevance based on when it was accessed.

Key Features and Tools

Intelligence Tools

• extract_github: Retrieve information such as README, stars, forks, language, topics, and last commit from any GitHub repository.
• extract_hackernews: Access top stories and search results from Hacker News, complete with scores and timestamps.
• extract_scholar: Gather research paper titles, authors, publication years, and snippets from Google Scholar.

Competitive Intelligence Tools

• extract_yc: Scrape Y Combinator company listings by keyword to discover funding activity in specific sectors.
• search_repos: Find similar or competing GitHub repositories ranked by stars and recent activity signals.
• package_trends: Analyze metadata from npm and PyPI package registries, including version history and last updated dates.

Composite Tool

• extract_landscape: Conduct a comprehensive query across YC startups, GitHub repositories, Hacker News sentiment, and the package ecosystem, yielding a unified landscape report.

Deployment Options

freshcontext-mcp can be deployed either locally using Playwright for comprehensive browser support or through a Cloudflare Workers setup for a serverless edge deployment.

Unique Value Proposition

The freshcontext-mcp addresses a fundamental gap in AI data retrieval by treating retrieval time as first-class metadata. It provides:

• retrieved_at: An exact ISO timestamp indicating when the data was accessed.
• content_date: The best estimate of the original publication date of the content.
• freshness_confidence: A measure of data reliability categorized as high, medium, or low.
• adapter: Identifies the source of the data, enhancing transparency.

This structure enables verifiable freshness of information, allowing users to differentiate between newly fetched and outdated content.

Roadmap and Contribution

The project roadmap includes further development of adapters for additional platforms and features, solidifying its position as an essential tool in AI intelligence gathering. Contributions, especially in the form of new adapters, are highly valued, promoting collaboration and enhancement of the toolset.

For developers and researchers seeking to refine AI models with accurate, timestamped data, the freshcontext-mcp project offers a robust solution to ensure reliability in information retrieval.