Purpose
freshprobe is used to perform freshness verification on the data sources used by AI agents, preventing agents from making decisions or taking actions based on stale data. It sits between the agent and the outside world, acting as a checkpoint for data freshness.
Unlike crashes, which cause alerts, stale data often produces confident, well-formed but completely wrong responses. Chain a few of these errors together in a multi-agent pipeline, and every component may report normal while the final output is catastrophically wrong. freshprobe is designed specifically to solve this problem.
Main Capabilities
According to official materials, freshprobe provides six verification signals and three deployment modes. The specific signal types and deployment mode details are not fully listed in the provided materials and are not yet available.
It can probe endpoints and check the following aspects:
- Cache staleness
- Latency
- TLS health
- Content changes
Probe results are returned as a deterministic JSON verdict, with a value of one of FRESH, STALE, or UNKNOWN.
Usage
Installation
Go install is recommended for installation. Prebuilt binaries for Linux, macOS, and Windows can also be downloaded from GitHub Releases (the specific commands and links are not provided in the materials).
Three Deployment Modes
- CLI command-line tool
- MCP server (for AI agents)
- HTTP microservice/API
MCP Server
freshprobe can be added to the AI tool configuration. The materials show examples of configuration in .cursor/mcp.json and .vscode/mcp.json (the specific configuration content is not yet provided). After configuration, three tools are exposed to the AI agent (the tool names are not yet provided).
Policies (Freshness as Code)
Freshness thresholds can be defined per domain via YAML. When a probe violates a policy, the corresponding action is triggered (the materials do not provide specific action details). There are four built-in policies:
- api-realtime
- api-standard
- static-content
- financial-data
Continuous Monitoring and Metrics
Supportscontinuous monitoring and provides sample output (content not yet provided). The HTTP server exposes a /metrics endpoint, providing Prometheus-compatible text-format metrics.
Kubernetes and Claude Code Plugin
Supports Kubernetes deployment (details not yet provided). A Claude Code plugin is provided; after installation, you can ask Claude questions such as: "Is the trading API returning fresh data?", "Check all our endpoints before running the batch job", "Does this API meet our real-time SLA?".
Applicable Scenarios
- Financial agents verify data freshness before using market quotes
- Customer service bots avoid making incorrect responses based on unsynchronized CRM data
- RAG pipelines avoid answering questions using stale documents
- Data freshness gating in multi-agent pipelines
- Freshness checks on all endpoints before batch jobs run
- Continuous monitoring of whether endpoints meet real-time SLAs
Limitations
The provided materials do not explain specific performance limitations, supported protocol scope, deployment environment requirements, fees, pricing, platform restrictions, or usage step details. Some content (such as the specific content of the six verification signals, the names of the three MCP tools, the specific actions after a policy violation, and sample output for Kubernetes deployment and continuous monitoring) is not fully given in the materials and is not yet available.
License and Contributions
The project uses the MIT license. Contribution directions include: policy packs for specific domains (healthcare, weather, finance), WebSocket/gRPC/GraphQL probe signals, OpenTelemetry integration, and a Homebrew formula.
