agentcheck

io.github.agentwares由 GitHub 帳號 agentwares 發布v0.1.1更新於 Oct 27, 2026

Synthetic checks, nightly regression replay and model-drift alerts for AI agents

已驗證Streamable HTTP可網頁執行開發者工具安全與監控AI 與機器學習

概覽

AI 產生的概覽

agentcheck 的遠端 MCP 伺服器,讓助理以合成檢查、夜間回歸重播和事件查詢來監控 AI 代理、MCP 伺服器與 API。

可以做什麼
  • 讀取公開狀態頁與受監控目標:整體狀態、正常運作時間、最近評分、未解決事件和徽章連結
  • 一次性預覽任意公開的 Atlassian Statuspage 頁面,含元件與未解決事件
  • 查詢各廠商棄用頁面上的模型退役與 API 版本停用資訊
  • 登記監控目標(HTTP 端點、遠端 MCP 伺服器、A2A 代理),新增檢查並立即執行
  • 記錄生產軌跡,把軌跡提升為可夜間重播的檢查,並列出未解決與近期事件
  • 取得機器可讀的方案價格與附加項目
適用情境
適合希望助理持續盯住 AI 代理、MCP 伺服器或 API 整合是否出現回歸、模型或 API 棄用以及故障,並把真實生產軌跡轉成定期檢查的情境。唯讀工具無需帳號即可查詢狀態和價格。
執行需求
以 Streamable HTTP 託管遠端端點執行,無需本機安裝。寫入類工具需要 API 金鑰,透過 Authorization 標頭以 Bearer 權杖傳送,須在 agentcheck 控制台建立。狀態與價格工具無需金鑰。需要能連線至 agentcheck 服務的網路。
安裝前請注意
Authorization 標頭攜帶有效 API 金鑰,請勿寫入日誌或共用設定。寫入類工具會建立目標與檢查、執行可能產生 LLM 評判費用的檢查,並把生產提示、回答和工具呼叫記錄到第三方服務。啟用 rubric 或 baseline 評判及夜間重播前請先確認價格。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 agentcheck,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.agentwares%2Fagentcheck/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "agentcheck": {
      "type": "http",
      "url": "https://agentwares-agentcheck.vercel.app/api/mcp"
    }
  }
}

README

該儲存庫沒有 README。

工具

11

agentcheck_get_status

Current status of a public monitored target: overall state, uptime over 24h/7d/30d, last check time, last nightly scores, open incidents and the badge/status URLs. Use the owner (GitHub login) and target slug from the status page URL https://agentwares-agentcheck.vercel.app/<owner>/<slug>. No API key needed.

輸入參數slug必填owner必填

agentcheck_get_pricing

Machine-readable pricing for agentcheck: tiers with monthly USD price, target limits, check interval and features, plus per-run add-ons. Same data as /pricing.json. No API key needed.

無需輸入參數。

agentcheck_model_retirements

Is this model retiring? Pass model IDs as your code or config names them (gpt-5-mini, claude-sonnet-4-5-20250929, gemini-2.5-flash, openai/o3-mini). For each, returns every announced shutdown on OpenAI's, Anthropic's and Google's own deprecation pages (OpenAI API, Claude API, Gemini API, Vertex AI): status, shutdown date, recommended replacement, the replacement's list-price ratio per token, the API changes the provider documents, the source URL with the date it was read, and a page with the details. retiring: false means no shutdown for that ID is on those pages. Language models only. No API key needed.

輸入參數models必填

agentcheck_api_sunsets

When does the API version this code pins stop working, and what happens then? Pass provider and version pairs as your code pins them (shopify 2025-10, meta v21.0, google-ads v22, hubspot v1, twilio-flex 2.16.2, twilio-notify v1, keap xml-rpc, exchange-online ews, stripe 2024-06-20). For each, returns every dated end the provider's own versioning, changelog or retirement page lists: status, end date (and whether the provider gives only a month), what happens after it (error, silent fall-forward to another version, app delisting, or unsupported), whether the failure is silent, the replacement version, the source URL with the date it was read, and a page with the details. Where the provider's own pages give different dates for the same end, other_dates lists each one with its page and date_note says which the counts run to. A pair with no entry comes back with the reason: older than every listed version, no end date published yet, or a provider that publishes none (Stripe). Covers Shopify, HubSpot, Meta Graph and Marketing APIs, Google Ads and Content API for Shopping, Twilio Flex UI and Notify, Thryv's Keap XML-RPC, and Exchange Web Services (EWS) in Exchange Online. No API key needed.

輸入參數pins必填

agentcheck_statuspage_preview

Read a public Atlassian Statuspage page once: its name, overall status, components (id, name, status, group) and unresolved incidents, from the page's own /api/v2/summary.json. Pass any address on the page: status.example.com, https://status.example.com/history or the JSON URL. Use it before POST /api/v1/statuspage, which turns the components you give a URL into a live agentcheck status page checked by agentcheck itself, with no signup; the answer carries that request's body. Same read as /statuspage/preview: only public addresses, 5-second timeout, 512 KB cap. Private pages cannot be read. No API key needed.

輸入參數url必填

agentcheck_create_target

Enroll something to monitor: an http endpoint (JSON or OpenAI-style chat), a remote MCP server (Streamable HTTP url) or an A2A agent (origin with /.well-known/agent-card.json). Pass `checks` to create checks in the same call (POST /api/v1/probe proposes three). Returns the target id, the public status page, the badge SVG URL and a README snippet. The first check runs on the next scheduled tick, within five minutes; call agentcheck_run_now to run immediately. Free tier: 1 target, hourly; Starter+: 5-minute checks. Requires an API key.

輸入參數urlauthkind必填nameslugalertschecksformatheadersisPublicmodelVarcorpusUrl另有 3 個

agentcheck_add_check

Add a check to one of your targets. A check runs an input (http path/prompt, mcp tool call, a2a message) on a schedule and judges the answer with a golden: exact, contains, regex and json_schema cost nothing; rubric and baseline use the LLM judge (baseline = same outcome as the last known-good answer). Returns the check id. Requires an API key.

輸入參數kind必填name必填inputgolden必填targetId必填intervalSec

agentcheck_run_now

Run every check of one of your targets immediately (outside the schedule) and return pass/fail per check with latency, judge cost and any incident opened or closed. Use it right after enrolling, or to confirm a fix. Requires an API key.

輸入參數targetId必填

agentcheck_record

Record one production interaction with your agent (the prompt or messages, the final answer, the tools it called, optionally the model) as a trace on a target. Call it from your agent or from a proxy in front of it after each task; promote a good trace with agentcheck_promote_trace to replay it nightly and catch regressions. Requires an API key.

輸入參數nameinput必填modeloutput必填targetId必填toolCalls

agentcheck_promote_trace

Turn an imported or recorded trace into a replayable check whose golden is the recorded outcome (tool sequence + final-answer rubric). The check runs daily and in the nightly replay (Pro). Returns the check. Requires an API key.

輸入參數traceId必填

agentcheck_list_incidents

Open and recent incidents across your targets (or one target): when they opened/closed, the failing check and the cause. Requires an API key.

輸入參數targetId

版本歷史

1
  1. v0.1.1最新Sep 16, 2026

評分

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