agentcheck
https://agentwares-agentcheck.vercel.app/api/mcp ↗
Description
Synthetic checks, nightly regression replay and model-drift alerts for AI agents
What our measurements say
agentcheck is a remote MCP server published at agentwares-agentcheck.vercel.app. It has been probed 6 times since 9/12/2026. It answered in 6 of them (100.0%), a near-uninterrupted record. Median response time is 362 ms, placing it among the faster endpoints. It offers a narrow, focused set of 8 tools. On the protocol side it still runs 2025-11-25 and has not moved to the newer spec.
Agent readability score
Can an LLM agent pick the right tool here — names, descriptions and parameter clarity are assessed.
Agent readability score
Strengths
- Tool names explicitly indicate their purpose (e.g., 'agentcheck_get_status' for status retrieval).
- Some tools provide detailed usage context (e.g., 'agentcheck_create_target' explains enrollment scenarios).
Weaknesses
- Descriptions for tools like 'agentcheck_get_pricing' lack contextual usage guidance.
- Parameters with type 'null' (e.g., 'auth' in 'agentcheck_create_target') or missing required/optional flags create ambiguity.
- No explicit error handling or constraint documentation in tool descriptions.
agentcheck_get_pricing— Description only states purpose without explaining when/why to use it.agentcheck_create_target— Parameters like 'auth' have type 'null' and no required/optional clarification.agentcheck_add_check— No mention of error scenarios for invalid 'golden' validation rules.
Verdict
Event narrative
Risk: low
Advice
Schema compliance
tools/list structure, inputSchema validity, and a functional smoke test — the components of the 0-100 score.
- tools/list schema valid
- inputSchema is valid JSON Schema
- Smoke test not run this round
- Cold-start: cold 164ms → warm 173ms (penalty 0ms)
Tools
Tools the server advertised in the latest measurement — measured, not catalog-claimed.
agentcheck_get_statusCurrent 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.
ownerstringrequiredslugstringrequired
agentcheck_get_pricingMachine-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.
agentcheck_create_targetEnroll 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 minute tick; call agentcheck_run_now to run immediately. Free tier: 1 target, hourly; Starter+: 5-minute checks. Requires an API key.
namestringslugstringkindstringrequiredurlstringpackageSpecstringagentCardUrlstringformatstringheadersobjectauthmodelVarstringmodelHeaderstringcorpusUrlstringalertsobjectisPublicbooleanchecksarray
agentcheck_add_checkAdd 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.
namestringrequiredkindstringrequiredinputobjectgoldenobjectrequiredintervalSecintegertargetIdstringrequired
agentcheck_run_nowRun 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.
targetIdstringrequired
agentcheck_recordRecord 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.
targetIdstringrequirednamestringinputrequiredoutputstringrequiredtoolCallsarraymodelstring
agentcheck_promote_traceTurn 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.
traceIdstringrequired
agentcheck_list_incidentsOpen and recent incidents across your targets (or one target): when they opened/closed, the failing check and the cause. Requires an API key.
targetIdstring
Availability history
Event log
Derived by comparing consecutive probes — changes in era, protocol version, build and reachability.
- Added to watch — era: Legacy
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[](https://mcpmetrics.io/servers/io-github-agentwares-agentcheck)<a href="https://mcpmetrics.io/servers/io-github-agentwares-agentcheck"><img src="https://mcpmetrics.io/badge/io.github.agentwares/agentcheck/era.svg" alt="mcpmetrics"></a>You are seeing the last 7 days. Sign up for the full history. Which check failed and why is in the dashboard.
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