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Star count
2 ★ · Sudhan30/freshprobe · read from the GitHub search API · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 2 ★ · 7 sources · mcp
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How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
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We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
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repository Sudhan30/freshprobe;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
Data freshness verification for AI agents. Stop your agents from acting on stale data. --- > *"I asked my agent to check flight prices. It gave me options. I booked one. The fare had changed 3 hours ago."* AI agents routinely act on stale data without knowing it. A financial agent queries cached quotes from 47 minutes ago. A support bot tells a customer their order doesn't exist because the CRM hasn't synced. An RAG pipeline confidently answers with yesterday's docs. **freshprobe** sits between your agent and the external world. Before the agent acts, it asks: *is this data fresh enough?* The answer is always a deterministic JSON verdict: **FRESH**, **STALE**, or **UNKNOWN**. Single Go binary. No dependencies. Runs as CLI, MCP server, or HTTP microservice. Why this matters | Problem | Cost | |---------|------| | E-commerce agent used 6-month-old product data | $5M+ revenue loss | | Enterprise RAG with overlapping refresh infrastructure | $340K/year wasted | | AI project failures from data quality issues | 60%+ of failures (Gartner) | Unlike crashes that trigger alerts, stale data produces **confident, well-formatted, completely wrong responses**. Chain a few of those in a multi-agent pipeline and every component reports green while the output is catastrophically wrong. Install **Go install (recommended):** **Docker:** **From source:** **GitHub Releases:** Download pre-built binaries for Linux, macOS, and Windows from Releases. Quick start Six verification signals | Signal | What it checks | |--------|---------------| | **HTTP cache headers** | Parses Last-Modified, Cache-Control, Age, ETag, Date, Expires. Computes 0.0 to 1.0 freshness score | | **Endpoint liveness** | Measures response latency (P50/P95/P99), status codes, body size, degradation patterns | | **Content fingerprinting** | SHA-256 hashes response bodies across repeated probes to detect stale caches | | **TLS certificate health** | Certificate validity, days remaining, OCSP stapling status | | **DNS resolution timing** | DNS lookup latency as infrastructure health signal | | **Redirect chain analysis** | Tracks 301/302/307/308 hops, detects stale CDN configs | Three deployment modes CLI MCP server (for AI agents) Add to your AI tool config: Claude Desktop / Claude Code Cursor In .cursor/mcp.json: VS Code (Copilot) In .vscode/mcp.json: This exposes three tools to AI agents: | Tool | Description | |------|-------------| | freshprobe_check | Probe a single endpoint. Returns JSON verdict | | freshprobe_batch | Probe multiple endpoints concurrently | | freshprobe_policy | Check an endpoint against a named freshness policy | HTTP server Policies (freshness-as-code) Define freshness thresholds per domain in YAML: When a probe violates a policy: Example output: Prometheus metrics The HTTP server exposes /metrics with Prometheus-compatible text format: How it compares | Feature | freshprobe | Uptime Kuma | Gatus | freshcontext-mcp |