io.github.PrinceGabriel-lgtm/freshcontext
Real-time web intelligence for AI agents. 11 tools, no API keys. GitHub, HN, Reddit, arXiv & more.
Ask AI about io.github.PrinceGabriel-lgtm/freshcontext
Powered by Claude Β· Grounded in docs
I know everything about io.github.PrinceGabriel-lgtm/freshcontext. Ask me about installation, configuration, usage, or troubleshooting.
0/500
Reviews
Documentation
freshcontext-mcp
I asked Claude to help me find a job. It gave me a list of openings. I applied to three of them. Two didn't exist anymore. One had been closed for two years.
Claude had no idea. It presented everything with the same confidence.
That's the problem freshcontext fixes.
The Standard
FreshContext is a data freshness layer for AI agents β an open standard and reference implementation that makes retrieved data trustworthy.
Every piece of web data an AI agent retrieves has an age. Most tools ignore it. FreshContext surfaces it β wrapping every result in a structured envelope that carries three guarantees:
[FRESHCONTEXT]
Source: https://github.com/owner/repo
Published: 2024-11-03
Retrieved: 2026-03-05T09:19:00Z
Confidence: high
---
... content ...
[/FRESHCONTEXT]
When it was retrieved. Where it came from. How confident we are the date is accurate.
The FreshContext Specification v1.1 is published as an open standard under MIT license. Any tool, agent, or system that wraps retrieved data in this envelope is FreshContext-compatible. β Read the spec
20 tools. No API keys.
Intelligence
| Tool | What it gets you |
|---|---|
extract_github | README, stars, forks, language, topics, last commit |
extract_hackernews | Top stories or search results with scores and timestamps |
extract_scholar | Research papers β titles, authors, years, snippets |
extract_arxiv | arXiv papers via official API β more reliable than Scholar |
extract_reddit | Posts and community sentiment from any subreddit |
Competitive research
| Tool | What it gets you |
|---|---|
extract_yc | YC company listings by keyword β who's funded in your space |
extract_producthunt | Recent launches by topic |
search_repos | GitHub repos ranked by stars with activity signals |
package_trends | npm and PyPI metadata β version history, release cadence |
Market data
| Tool | What it gets you |
|---|---|
extract_finance | Live stock data β price, market cap, P/E, 52w range. Up to 5 tickers. |
search_jobs | Remote job listings from Remotive, RemoteOK, HN "Who is Hiring" β every listing dated |
Composites β multiple sources, one call
| Tool | Sources | What it gets you |
|---|---|---|
extract_landscape | 6 | YC + GitHub + HN + Reddit + Product Hunt + npm in parallel |
extract_idea_landscape | 6 | HN + YC + GitHub + Jobs + npm + Product Hunt β full idea validation |
extract_gov_landscape | 4 | Gov contracts + HN + GitHub + changelog |
extract_finance_landscape | 5 | Finance + HN + Reddit + GitHub + changelog |
extract_company_landscape | 5 | The full picture on any company β see below |
Unique β not available in any other MCP server
| Tool | Source | What it gets you |
|---|---|---|
extract_changelog | GitHub Releases API / npm / auto-discover | Update history from any repo, package, or website |
extract_govcontracts | USASpending.gov | US federal contract awards β company, amount, agency, period |
extract_sec_filings | SEC EDGAR | 8-K filings β legally mandated material event disclosures |
extract_gdelt | GDELT Project | Global news intelligence β 100+ languages, every country, 15-min updates |
extract_gebiz | data.gov.sg | Singapore Government procurement tenders β open dataset, no auth |
extract_idea_landscape
Built for the moment before you start building. Six sources fired in parallel to answer: should I build this?
- Hacker News β what are developers actively complaining about (pain signal)
- YC Companies β who has already received funding in this space (funding signal)
- GitHub β how crowded the open source landscape is (crowding signal)
- Job listings β companies hiring around this problem = real budget = real market (market signal)
- npm / PyPI β ecosystem adoption and release velocity (ecosystem signal)
- Product Hunt β what just launched and how the market received it (launch signal)
Use extract_idea_landscape with idea "data freshness for AI agents"
extract_company_landscape
The most complete single-call company analysis available in any MCP server. Five sources fired in parallel:
- SEC EDGAR β what did they legally just disclose (8-K filings)
- USASpending.gov β who is giving them government money
- GDELT β what is global news saying right now
- Changelog β are they actually shipping product
- Yahoo Finance β what is the market pricing in
Use extract_company_landscape with company "Palantir" and ticker "PLTR"
Real output from March 2026:
Q4 2025: Revenue $1.407B (+70% YoY). US commercial +137%. Rule of 40 score: 127%. Federal contracts: $292.7M Army Maven Smart System Β· $252.5M CDAO Β· $145M ICE Β· $130M Air Force Β· more SEC filing: Q4 earnings 8-K filed Feb 3, 2026 β GAAP net income $609M, 43% margin GDELT: ICE/Medicaid data controversy, UK MoD security warning, NHS opposition β all timestamped PLTR: ~$154β157 Β· Market cap ~$370B Β· P/E 244x Β· 52w range $66 β $207
Bloomberg Terminal doesn't read commit history as a company health signal. FreshContext does.
Quick Start
Option A β Cloud (no install)
Add to your Claude Desktop config and restart:
Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"freshcontext": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://freshcontext-mcp.gimmanuel73.workers.dev/mcp"]
}
}
}
Restart Claude. Done.
Prefer a guided setup? Visit freshcontext-site.pages.dev β 3 steps, no terminal.
Option B β Local (full Playwright)
Requires: Node.js 18+ (nodejs.org)
git clone https://github.com/PrinceGabriel-lgtm/freshcontext-mcp
cd freshcontext-mcp
npm install
npx playwright install chromium
npm run build
Add to Claude Desktop config:
Mac:
{
"mcpServers": {
"freshcontext": {
"command": "node",
"args": ["/Users/YOUR_USERNAME/path/to/freshcontext-mcp/dist/server.js"]
}
}
}
Windows:
{
"mcpServers": {
"freshcontext": {
"command": "node",
"args": ["C:\\Users\\YOUR_USERNAME\\path\\to\\freshcontext-mcp\\dist\\server.js"]
}
}
}
Troubleshooting (Mac)
"command not found: node" β Use the full path:
which node # copy this output, replace "node" in config
Config file doesn't exist β Create it:
mkdir -p ~/Library/Application\ Support/Claude
touch ~/Library/Application\ Support/Claude/claude_desktop_config.json
Usage examples
Should I build this idea?
Use extract_idea_landscape with idea "procurement intelligence saas"
Returns funding signal, pain signal, crowding signal, market signal, ecosystem signal, and launch signal β all timestamped.
Full company intelligence in one call:
Use extract_company_landscape with company "Palantir" and ticker "PLTR"
SEC filings + federal contracts + global news + changelog + market data. The complete picture.
Is anyone already building what you're building?
Use extract_landscape with topic "cashflow prediction saas"
Returns who's funded, what's trending, what repos exist, what packages are moving β all timestamped.
What's Singapore's government procuring right now?
Use extract_gebiz with url "artificial intelligence"
Returns live tenders from the Ministry of Finance open dataset β agency, amount, closing date, all timestamped.
Did that company just disclose something material?
Use extract_sec_filings with url "Palantir Technologies"
8-K filings are legally mandated within 4 business days of any material event β CEO change, acquisition, breach, major contract.
What is global news saying about a company right now?
Use extract_gdelt with url "Palantir"
100+ languages, every country, updated every 15 minutes. Surfaces what Western sources miss.
Which companies just won US government contracts in AI?
Use extract_govcontracts with url "artificial intelligence"
Largest recent federal contract awards matching that keyword β company, amount, agency, award date.
Is this dependency still actively maintained?
Use extract_changelog with url "https://github.com/org/repo"
Returns the last 8 releases with exact dates. If the last release was 18 months ago, you'll know before you pin the version.
How freshness works
Most AI tools retrieve data silently. No timestamp, no signal, no way for the agent to know how old it is.
FreshContext treats retrieval time as first-class metadata. Every adapter returns:
retrieved_atβ exact ISO timestamp of the fetchcontent_dateβ best estimate of when the content was originally publishedfreshness_confidenceβhigh,medium, orlowbased on signal qualityfreshness_scoreβ numeric 0β100 with domain-specific decay rates (financial data at 5.0, academic papers at 0.3)adapterβ which source the data came from
When confidence is high, the date came from a structured field (API, metadata). When it's medium or low, FreshContext tells you why.
Security
- Input sanitization and domain allowlists on all adapters
- SSRF prevention (blocked private IP ranges)
- KV-backed global rate limiting: 60 req/min per IP across all edge nodes
- No credentials required β all public data sources
Roadmap
- 20 tools across intelligence, competitive research, market data, and composites
-
extract_changelogβ update cadence from any repo, package, or website -
extract_govcontractsβ US federal contract intelligence via USASpending.gov -
extract_sec_filingsβ SEC EDGAR 8-K material event filings -
extract_gdeltβ GDELT global news intelligence (100+ languages) -
extract_gebizβ Singapore Government procurement via data.gov.sg -
extract_company_landscapeβ 5-source company intelligence composite -
extract_idea_landscapeβ 6-source idea validation composite -
freshness_scorenumeric metric (0β100) with domain-specific decay rates - Cloudflare Workers deployment β global edge with KV caching and rate limiting
- D1 database β 18 watched queries running on 6-hour cron with relevancy scoring
- Listed on official MCP Registry
- Listed on Apify Store
- FreshContext Specification v1.1 published (MIT) β composite adapters, decay rate table, compatibility levels
- GitHub Actions CI/CD β auto-publish to npm on every push
- DAR engine β exponential decay scoring with proprietary Ξ» constants (v0.3.15)
- Ha-Pri audit signatures β SHA-256 provenance stamps on every signal
- Semantic deduplication β cross-adapter fingerprinting
- Intelligence feed endpoint β
/v1/intel/feed/:profile_id - METHODOLOGY.md β formal IP documentation
- Webhook triggers β push high-entropy signals on threshold
- Domain-specific watched queries for mining/industrial sector
- Subscription tier with profile customization
- GKG upgrade for
extract_gdeltβ tone scores, goldstein scale, event codes - Dashboard β React frontend for the D1 intelligence pipeline
Contributing
PRs welcome. New adapters are the highest-value contribution β see src/adapters/ for the pattern and FRESHCONTEXT_SPEC.md for the contract any adapter must fulfill.
If you're building something FreshContext-compatible, open an issue and we'll add you to the ecosystem list.
License
MIT
Built by Prince Gabriel β Grootfontein, Namibia π³π¦ "The work isn't gone. It's just waiting to be continued."
Also on: Apify Store Β· MCP Registry Β· npm
The Intelligence Layer (v0.3.15)
FreshContext is no longer just a pull tool. The infrastructure now runs a continuous Decay-Adjusted Relevancy (DAR) engine that scores every signal with exponential decay and provenance signatures.
The math
R_t = R_0 Β· e^(-Ξ»t)
R_0β base semantic score against your profile (0β100)Ξ»β source-specific decay constant (per hour)tβ hours since the content was publishedR_tβ final relevancy at query time
Source half-lives are calibrated empirically: Hacker News β14h, Reddit β3d, jobs β6d, GitHub β5mo, academic papers β1.6y.
What every signal carries
Every row in the D1 ledger is stamped with:
base_scoreβ R_0, semantic match against profilert_scoreβ R_t, decay-adjusted relevancyentropy_levelβlow/stable/highon the decay curveha_pri_sigβ SHA-256 provenance signature (tamper-evident)semantic_fingerprintβ cross-adapter deduplication hashpublished_atβ extracted content publication date
The intelligence feed
GET /v1/intel/feed/:profile_id?limit=20&min_rt=0
Returns scored, deduplicated, provenance-stamped signals ranked by R_t β ready for direct consumption by any LLM or agent. No synthesis needed.
Methodology
The full data collection, scoring, and provenance methodology is formally documented in METHODOLOGY.md β written as an audit trail for acquirers, integrators, and regulators. Version 1.1, April 2026.
Live endpoints
| Endpoint | Method | Purpose |
|---|---|---|
/ | GET | Service info + endpoint list |
/health | GET | Liveness check |
/mcp | POST | MCP JSON-RPC transport |
/briefing | GET | Latest stored briefing |
/briefing/now | POST | Force scrape + synthesize |
/v1/intel/feed/:profile_id | GET | DAR-scored intelligence feed |
/watched-queries | GET | List all watched queries |
/debug/db | GET | D1 counts + DAR engine coverage |
/debug/scrape | GET | Run a single adapter raw |
Production: https://freshcontext-mcp.gimmanuel73.workers.dev
