How does freshprobe detect stale data?

By analyzing HTTP cache headers, latency metrics, content fingerprints, TLS health, DNS timing, and redirect chains.

Can freshprobe run as an MCP server for AI agents?

Yes, it supports a mode to run as an MCP server via stdio for integration with AI tools.

Does freshprobe require external dependencies?

No, it is a single Go binary with no external dependencies.

Freshprobe

Q: How can I install freshprobe?

Install via 'go install github.com/Sudhan30/freshprobe/cmd/freshprobe@latest', Docker image, or build from source.

monitoringfreshnesshttpcachemcp

Verifies data freshness of HTTP endpoints with deterministic FRESH/STALE/UNKNOWN verdicts via CLI, MCP server, or HTTP API.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾

No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for Sudhan30/freshprobe, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.

Overview

freshprobe checks HTTP endpoints for data freshness by analyzing cache headers, latency, content changes, TLS health, DNS timing, and redirects. It returns deterministic JSON verdicts indicating if data is fresh, stale, or unknown, helping AI agents avoid acting on outdated information. Use it to monitor API data freshness continuously or on-demand with configurable policies and Prometheus metrics.

Use cases

Check freshness of API responses before AI agent actions

Continuously monitor endpoint freshness and alert on changes

Batch verify multiple endpoints for cache staleness

Fingerprint content to detect unchanged or stale data

Integrate freshness checks into AI pipelines via MCP server

Key features

Deterministic freshness verdicts with confidence scores

Analyzes HTTP cache headers and computes freshness scores

Measures endpoint latency percentiles and error rates

Content fingerprinting via repeated SHA-256 hashing

TLS certificate validity and OCSP stapling checks

DNS resolution timing and redirect chain analysis

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Freshprobe.

Extracted Tool Capabilities

Deterministic freshness verdicts with confidence scores

Analyzes HTTP cache headers and computes freshness scores

Measures endpoint latency percentiles and error rates

Content fingerprinting via repeated SHA-256 hashing

TLS certificate validity and OCSP stapling checks

DNS resolution timing and redirect chain analysis

Documentation Overview

freshprobe

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.

Code

$ freshprobe check https://api.example.com/v2/quotes

{
  "verdict": "STALE",
  "confidence": 0.94,
  "endpoint": "https://api.example.com/v2/quotes",
  "freshness": {
    "data_age_seconds": 2847,
    "freshness_score": 0.12,
    "cache_control": "max-age=3600"
  },
  "liveness": {
    "status": "DEGRADED",
    "latency_p50_ms": 342,
    "latency_p95_ms": 1847,
    "body_size_bytes": 4096,
    "error_rate": 0.03
  },
  "redirects": {
    "total_hops": 1,
    "final_url": "https://api-v2.example.com/quotes",
    "has_redirect": true
  },
  "nist_mapping": {
    "ai_rmf_function": "MEASURE",
    "control": "MS-2.6-001"
  }
}

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):

bash

go install github.com/Sudhan30/freshprobe/cmd/freshprobe@latest

Docker:

Terminal

docker run --rm ghcr.io/sudhan30/freshprobe:latest check https://example.com

From source:

bash

git clone https://github.com/Sudhan30/freshprobe.git && cd freshprobe && make build
./bin/freshprobe --version

GitHub Releases: Download pre-built binaries for Linux, macOS, and Windows from Releases.

Quick start

bash

# Basic freshness check
freshprobe check https://api.example.com/data

# Human-readable output
freshprobe check https://api.example.com/data --output text

# Content fingerprinting: detect if data actually changes
freshprobe check https://api.example.com/data --repeat 3 --interval 2s

# Check against a freshness policy
freshprobe check https://api.example.com/data --policy-dir ./policies --policy financial-data

# Batch check multiple endpoints
freshprobe batch https://api1.example.com https://api2.example.com https://cdn.example.com

# Continuous monitoring (Ctrl+C to stop)
freshprobe watch https://api.example.com/data --interval 30s --output text

# Only alert on verdict changes (FRESH -> STALE)
freshprobe watch https://api.example.com/data --interval 1m --on-change --output text

# View probe history for an endpoint
freshprobe history https://api.example.com/data --limit 20 --output text

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

bash

freshprobe check <url> [flags]
freshprobe batch <urls...> [flags]
freshprobe watch <url> --interval 30s [flags]
freshprobe history <url> --limit 20

MCP server (for AI agents)

Add to your AI tool config:

Claude Desktop / Claude Code

config.json

{
  "freshprobe": {
    "type": "stdio",
    "command": "freshprobe",
    "args": ["serve", "--mode", "mcp", "--policy-dir", "/path/to/policies", "--stateless"]
  }
}

Cursor

In .cursor/mcp.json:

config.json

{
  "mcpServers": {
    "freshprobe": {
      "command": "freshprobe",
      "args": ["serve", "--mode", "mcp", "--stateless"]
    }
  }
}

VS Code (Copilot)

In .vscode/mcp.json:

config.json

{
  "servers": {
    "freshprobe": {
      "type": "stdio",
      "command": "freshprobe",
      "args": ["serve", "--mode", "mcp", "--stateless"]
    }
  }
}

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

bash

freshprobe serve --mode http --addr :8080

Code

POST /api/v1/check    {"url": "https://..."}
POST /api/v1/batch    {"urls": ["https://...", "https://..."]}
POST /api/v1/policy   {"url": "https://...", "policy_name": "api-realtime"}
GET  /healthz
GET  /metrics          # Prometheus-compatible metrics

Policies (freshness-as-code)

Define freshness thresholds per domain in YAML:

yaml

version: "1"
policies:
  financial-data:
    name: "Financial Data"
    domains: ["*.market.*", "*.trading.*"]
    max_staleness: "30s"
    min_freshness_score: 0.9
    max_latency_p95_ms: 200
    require_tls: true
    min_tls_days_left: 30
    require_changing: true

  api-standard:
    name: "Standard API"
    domains: ["api.*"]
    max_staleness: "5m"
    min_freshness_score: 0.6
    max_latency_p95_ms: 2000
    require_tls: true

When a probe violates a policy:

config.json

{
  "policy_result": {
    "policy_name": "Financial Data",
    "passed": false,
    "violations": [
      {"check": "max_staleness", "expected": "<= 30s", "actual": "2m15s"},
      {"check": "max_latency_p95", "expected": "<= 200 ms", "actual": "847 ms"}
    ]
  }
}

Four built-in policies included: api-realtime, api-standard, static-content, financial-data.

Continuous monitoring

bash

# Watch an endpoint, print every probe
freshprobe watch https://api.example.com/quotes --interval 30s --output text

# Only print when verdict changes (FRESH -> STALE transitions)
freshprobe watch https://api.example.com/quotes --interval 1m --on-change --output text

# Run 10 probes and exit
freshprobe watch https://api.example.com/quotes --count 10 --interval 5s

Example output:

Code

Watching https://api.example.com/quotes every 30s
[14:22:01] FRESH conf=0.90 score=0.87 p95=142ms
[14:22:31] FRESH conf=0.90 score=0.85 p95=156ms
[14:23:01] STALE conf=0.85 score=0.22 p95=1847ms [FRESH -> STALE]

Prometheus metrics

The HTTP server exposes /metrics with Prometheus-compatible text format:

Code

freshprobe_probes_total 142
freshprobe_verdict_total{verdict="FRESH"} 98
freshprobe_verdict_total{verdict="STALE"} 31
freshprobe_verdict_total{verdict="UNKNOWN"} 13
freshprobe_latency_p95_seconds 0.234000
freshprobe_freshness_score 0.7200

How it compares

Feature	freshprobe	Uptime Kuma	Gatus	freshcontext-mcp
Purpose	Data freshness for AI agents	Uptime monitoring	Health dashboards	Web extraction timestamps
Knows data is stale	Yes (cache headers + fingerprinting)	No (only checks HTTP status)	No (only checks response assertions)	Partial (timestamps, no verification)
MCP server	Yes (3 tools)	No	No	Yes
Policy engine	Yes (YAML, per-domain)	No	Yes (YAML conditions)	No
Continuous monitoring	Yes (`watch` command)	Yes (dashboard)	Yes (dashboard)	No
Prometheus metrics	Yes	No (push-based)	Yes	No
Deployment	Single binary	Docker + DB	Single binary	npm package

Architecture

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

5mo ago

Most recent push to the default branch.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Freshprobe

It returns deterministic JSON verdicts: FRESH, STALE, or UNKNOWN with confidence scores.

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Technical Specs & Signals

CategoryMonitoring

PricingFree

4/5 checks healthy over the last 32d

44Quality signal: Fair · 44/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools21/30

Adoption & activity2/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 📊 Monitoring →Best MCP servers for Monitoring & Observability →Alternatives to Freshprobe →Install in Claude Desktop Install in Cursor Install in VS Code

Freshprobe

monitoringfreshnesshttpcachemcp

Verifies data freshness of HTTP endpoints with deterministic FRESH/STALE/UNKNOWN verdicts via CLI, MCP server, or HTTP API.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Manual Client & Custom JSON ConfigExpand JSON ▾

Overview

Use cases

Check freshness of API responses before AI agent actions

Continuously monitor endpoint freshness and alert on changes

Batch verify multiple endpoints for cache staleness

Fingerprint content to detect unchanged or stale data

Integrate freshness checks into AI pipelines via MCP server

Key features

Deterministic freshness verdicts with confidence scores

Analyzes HTTP cache headers and computes freshness scores

Measures endpoint latency percentiles and error rates

Content fingerprinting via repeated SHA-256 hashing

TLS certificate validity and OCSP stapling checks

DNS resolution timing and redirect chain analysis

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Freshprobe.

Extracted Tool Capabilities

Deterministic freshness verdicts with confidence scores

Analyzes HTTP cache headers and computes freshness scores

Measures endpoint latency percentiles and error rates

Content fingerprinting via repeated SHA-256 hashing

TLS certificate validity and OCSP stapling checks

DNS resolution timing and redirect chain analysis

Documentation Overview

freshprobe

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."

Code

$ freshprobe check https://api.example.com/v2/quotes

{
  "verdict": "STALE",
  "confidence": 0.94,
  "endpoint": "https://api.example.com/v2/quotes",
  "freshness": {
    "data_age_seconds": 2847,
    "freshness_score": 0.12,
    "cache_control": "max-age=3600"
  },
  "liveness": {
    "status": "DEGRADED",
    "latency_p50_ms": 342,
    "latency_p95_ms": 1847,
    "body_size_bytes": 4096,
    "error_rate": 0.03
  },
  "redirects": {
    "total_hops": 1,
    "final_url": "https://api-v2.example.com/quotes",
    "has_redirect": true
  },
  "nist_mapping": {
    "ai_rmf_function": "MEASURE",
    "control": "MS-2.6-001"
  }
}

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)

Install

Go install (recommended):

bash

go install github.com/Sudhan30/freshprobe/cmd/freshprobe@latest

Docker:

Terminal

docker run --rm ghcr.io/sudhan30/freshprobe:latest check https://example.com

From source:

bash

git clone https://github.com/Sudhan30/freshprobe.git && cd freshprobe && make build
./bin/freshprobe --version

GitHub Releases: Download pre-built binaries for Linux, macOS, and Windows from Releases.

Quick start

bash

# Basic freshness check
freshprobe check https://api.example.com/data

# Human-readable output
freshprobe check https://api.example.com/data --output text

# Content fingerprinting: detect if data actually changes
freshprobe check https://api.example.com/data --repeat 3 --interval 2s

# Check against a freshness policy
freshprobe check https://api.example.com/data --policy-dir ./policies --policy financial-data

# Batch check multiple endpoints
freshprobe batch https://api1.example.com https://api2.example.com https://cdn.example.com

# Continuous monitoring (Ctrl+C to stop)
freshprobe watch https://api.example.com/data --interval 30s --output text

# Only alert on verdict changes (FRESH -> STALE)
freshprobe watch https://api.example.com/data --interval 1m --on-change --output text

# View probe history for an endpoint
freshprobe history https://api.example.com/data --limit 20 --output text

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

bash

freshprobe check <url> [flags]
freshprobe batch <urls...> [flags]
freshprobe watch <url> --interval 30s [flags]
freshprobe history <url> --limit 20

MCP server (for AI agents)

Add to your AI tool config:

Claude Desktop / Claude Code

config.json

{
  "freshprobe": {
    "type": "stdio",
    "command": "freshprobe",
    "args": ["serve", "--mode", "mcp", "--policy-dir", "/path/to/policies", "--stateless"]
  }
}

Cursor

In .cursor/mcp.json:

config.json

{
  "mcpServers": {
    "freshprobe": {
      "command": "freshprobe",
      "args": ["serve", "--mode", "mcp", "--stateless"]
    }
  }
}

VS Code (Copilot)

In .vscode/mcp.json:

config.json

{
  "servers": {
    "freshprobe": {
      "type": "stdio",
      "command": "freshprobe",
      "args": ["serve", "--mode", "mcp", "--stateless"]
    }
  }
}

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

bash

freshprobe serve --mode http --addr :8080

Code

POST /api/v1/check    {"url": "https://..."}
POST /api/v1/batch    {"urls": ["https://...", "https://..."]}
POST /api/v1/policy   {"url": "https://...", "policy_name": "api-realtime"}
GET  /healthz
GET  /metrics          # Prometheus-compatible metrics

Policies (freshness-as-code)

Define freshness thresholds per domain in YAML:

yaml

version: "1"
policies:
  financial-data:
    name: "Financial Data"
    domains: ["*.market.*", "*.trading.*"]
    max_staleness: "30s"
    min_freshness_score: 0.9
    max_latency_p95_ms: 200
    require_tls: true
    min_tls_days_left: 30
    require_changing: true

  api-standard:
    name: "Standard API"
    domains: ["api.*"]
    max_staleness: "5m"
    min_freshness_score: 0.6
    max_latency_p95_ms: 2000
    require_tls: true

When a probe violates a policy:

config.json

{
  "policy_result": {
    "policy_name": "Financial Data",
    "passed": false,
    "violations": [
      {"check": "max_staleness", "expected": "<= 30s", "actual": "2m15s"},
      {"check": "max_latency_p95", "expected": "<= 200 ms", "actual": "847 ms"}
    ]
  }
}

Four built-in policies included: api-realtime, api-standard, static-content, financial-data.

Continuous monitoring

bash

# Watch an endpoint, print every probe
freshprobe watch https://api.example.com/quotes --interval 30s --output text

# Only print when verdict changes (FRESH -> STALE transitions)
freshprobe watch https://api.example.com/quotes --interval 1m --on-change --output text

# Run 10 probes and exit
freshprobe watch https://api.example.com/quotes --count 10 --interval 5s

Example output:

Code

Watching https://api.example.com/quotes every 30s
[14:22:01] FRESH conf=0.90 score=0.87 p95=142ms
[14:22:31] FRESH conf=0.90 score=0.85 p95=156ms
[14:23:01] STALE conf=0.85 score=0.22 p95=1847ms [FRESH -> STALE]

Prometheus metrics

The HTTP server exposes /metrics with Prometheus-compatible text format:

Code

freshprobe_probes_total 142
freshprobe_verdict_total{verdict="FRESH"} 98
freshprobe_verdict_total{verdict="STALE"} 31
freshprobe_verdict_total{verdict="UNKNOWN"} 13
freshprobe_latency_p95_seconds 0.234000
freshprobe_freshness_score 0.7200

How it compares

Feature	freshprobe	Uptime Kuma	Gatus	freshcontext-mcp
Purpose	Data freshness for AI agents	Uptime monitoring	Health dashboards	Web extraction timestamps
Knows data is stale	Yes (cache headers + fingerprinting)	No (only checks HTTP status)	No (only checks response assertions)	Partial (timestamps, no verification)
MCP server	Yes (3 tools)	No	No	Yes
Policy engine	Yes (YAML, per-domain)	No	Yes (YAML conditions)	No
Continuous monitoring	Yes (`watch` command)	Yes (dashboard)	Yes (dashboard)	No
Prometheus metrics	Yes	No (push-based)	Yes	No
Deployment	Single binary	Docker + DB	Single binary	npm package

Architecture

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

5mo ago

Most recent push to the default branch.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Freshprobe

It returns deterministic JSON verdicts: FRESH, STALE, or UNKNOWN with confidence scores.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/sudhan30-freshprobe?style=directory)](https://allmcps.com/mcp/sudhan30-freshprobe)

HTML Embed

<a href="https://allmcps.com/mcp/sudhan30-freshprobe"><img src="https://allmcps.com/api/badge/sudhan30-freshprobe?style=directory" alt="Freshprobe on AllMCPs" /></a>

Technical Specs & Signals

CategoryMonitoring

PricingFree

4/5 checks healthy over the last 32d

44Quality signal: Fair · 44/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools21/30

Adoption & activity2/15

Community engagement0/10

★ Featured

AllMCPs Server

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 📊 Monitoring →Best MCP servers for Monitoring & Observability →Alternatives to Freshprobe →Install in Claude Desktop Install in Cursor Install in VS Code