# agentcheck (remote · agentwares-agentcheck.vercel.app)

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

- Trust score: 73/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-25

## Components

- remote · `agentwares-agentcheck.vercel.app`: 73/100 (this document), [markdown](https://verifymcp.io/servers/agentwares-agentcheck/api-mcp.md), [page](https://verifymcp.io/servers/agentwares-agentcheck/api-mcp)

## Channel facts

- Endpoint: `https://agentwares-agentcheck.vercel.app/api/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.1.1`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-25.

- **Endpoint Security**: 63/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 8 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 66/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 1489 tokens (~186/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 60/100
  - Stability observed for 18 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 8 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 9 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the agentcheck MCP server?

agentcheck is a hosted endpoint at https://agentwares-agentcheck.vercel.app/api/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http agentwares-agentcheck 'https://agentwares-agentcheck.vercel.app/api/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "agentwares-agentcheck": {
      "url": "https://agentwares-agentcheck.vercel.app/api/mcp"
    }
  }
}
```

### VS Code

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

### Codex

```toml
[mcp_servers.agentwares-agentcheck]
url = "https://agentwares-agentcheck.vercel.app/api/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "agentwares-agentcheck": {
      "type": "remote",
      "url": "https://agentwares-agentcheck.vercel.app/api/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add agentwares-agentcheck --url 'https://agentwares-agentcheck.vercel.app/api/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  agentwares-agentcheck:
    url: "https://agentwares-agentcheck.vercel.app/api/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "agentwares-agentcheck": {
      "Transport": "http",
      "Url": "https://agentwares-agentcheck.vercel.app/api/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add agentwares-agentcheck -t streamable-http -u 'https://agentwares-agentcheck.vercel.app/api/mcp'
```

### Other

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

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-25 (score 73, 0)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-09-23 (score 73, +1)

No change was recorded against any check on this day. Stability & Change Management went from 50 to 53. That category is still filling its 30-day observation window: 15 days of observed history at the previous scan, 16 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-21 (score 72, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-19 (score 71, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-17 (score 70, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-15 (score 69, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-13 (score 68, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-11 (score 67, +1)

- [functional regression] Schema quality: 168 → 186
- [functional improvement] Tool coverage: 81% → 100%
- [functional] Schema quality: good → excellent
- [functional] Server version: 0.1.0 → 0.1.1
- [cosmetic] “agentcheck_add_check” reworded the description of “golden”
- [cosmetic] “agentcheck_create_target” reworded the description of “alerts”
- [cosmetic] “agentcheck_create_target” reworded the description of “auth”
- [cosmetic] “agentcheck_list_incidents” reworded the description of “targetId”
- [cosmetic] “agentcheck_record” reworded the description of “model”
- [cosmetic] “agentcheck_record” reworded the description of “targetId”

## MCP tools (8)

### `agentcheck_get_status` (~118 tokens)

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.

Input parameters:

- `owner` (string, required): GitHub login of the target's owner, e.g. demo
- `slug` (string, required): target slug, e.g. demo

### `agentcheck_get_pricing` (~50 tokens)

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.

### `agentcheck_create_target` (~524 tokens)

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 minute tick; call agentcheck_run_now to run immediately. Free tier: 1 target, hourly; Starter+: 5-minute checks. Requires an API key.

Input parameters:

- `agentCardUrl` (string): a2a: explicit agent card URL when it is not at /.well-known/agent-card.json
- `alerts` (object): where to send an incident when a check starts failing. Any combination; omit it and incidents are visible only on the status page.
- `auth`: credential the prober sends to your endpoint: {type:'bearer',token} or {type:'header',name,value}. Stored encrypted and never returned or logged.
- `checks` (array): checks to create right away (the probe proposes three)
- `corpusUrl` (string): RAG targets: public corpus URL for the nightly groundedness scorer (Pro)
- `format` (string): http: openai_chat (POST /chat/completions body), json (raw POST), get (plain fetch)
- `headers` (object): extra request headers
- `isPublic` (boolean): public status page + badge (default true)
- `kind` (string, required): http (JSON or chat endpoint), mcp (Streamable HTTP url, or npx/uvx package spec), a2a (agent card)
- `modelHeader` (string): request header your endpoint accepts to override the model (drift re-runs try the new model)
- `modelVar` (string): the model your agent runs on (e.g. claude-sonnet-5); enables model-drift re-runs
- `name` (string): display name; defaults to the host
- `packageSpec` (string): mcp only: `npx @org/server` / `uvx server` — runs on the mcpcheck runner, not the minute checks
- `slug` (string): URL slug for /<owner>/<slug>; defaults to a slug of the name
- `url` (string): endpoint URL (http/mcp) or the agent's origin (a2a)

### `agentcheck_add_check` (~277 tokens)

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.

Input parameters:

- `golden` (object, required): how the answer is judged. exact / contains / regex / json_schema are free and deterministic; rubric and baseline call the LLM judge, and baseline compares against the last known-good answer.
- `input` (object): http: { path?, method?, body?, prompt? } · mcp_tool_call: { tool, args } · a2a_task: { message }
- `intervalSec` (integer): seconds between runs; the tier's interval is the floor (Free hourly, Starter+ 5 min)
- `kind` (string, required): what to run: http (GET path or POST prompt), mcp_tools_list, mcp_tool_call (tool + args), a2a_task (message)
- `name` (string, required): short name shown on the status page
- `targetId` (string, required): id from agentcheck_create_target or GET /api/v1/targets

### `agentcheck_run_now` (~70 tokens)

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.

Input parameters:

- `targetId` (string, required): target id

### `agentcheck_record` (~191 tokens)

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.

Input parameters:

- `input` (required): the prompt, the messages array, or an object with prompt/messages
- `model` (string): the model that produced this answer, so drift re-runs can compare across models
- `name` (string): short label, e.g. 'refund for order A-1029'
- `output` (string, required): the agent's final answer
- `targetId` (string, required): id from agentcheck_create_target or GET /api/v1/targets
- `toolCalls` (array): tool calls in order

### `agentcheck_promote_trace` (~84 tokens)

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.

Input parameters:

- `traceId` (string, required): trace id from agentcheck_record or POST /api/v1/targets/{id}/traces

### `agentcheck_list_incidents` (~63 tokens)

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

Input parameters:

- `targetId` (string): narrow to one target; omit for incidents across every target on the account

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/agentwares-agentcheck/api-mcp#diagnostics

## Score history

- 2026-09-25: 73
- 2026-09-24: 73
- 2026-09-23: 73
- 2026-09-22: 72
- 2026-09-21: 72
- 2026-09-20: 71
- 2026-09-19: 71
- 2026-09-18: 70
- 2026-09-17: 70
- 2026-09-16: 69
- 2026-09-15: 69
- 2026-09-14: 68
- 2026-09-13: 68
- 2026-09-12: 67
- 2026-09-11: 67
- 2026-09-10: 66
- 2026-09-09: 66
- 2026-09-08: 65
- 2026-09-07: 65

## Common questions

### What is the agentcheck MCP server?

agentcheck is an MCP server listed in the public MCP registry as io.github.agentwares/agentcheck. Synthetic checks, nightly regression replay and model-drift alerts for AI agents. This page covers its hosted endpoint (https://agentwares-agentcheck.vercel.app/api/mcp).

### Is the agentcheck MCP server safe to use?

agentcheck scores 73 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the agentcheck MCP server expose?

agentcheck exposes 8 tools: agentcheck_get_status, agentcheck_get_pricing, agentcheck_create_target, agentcheck_add_check, agentcheck_run_now, and 3 more. Their descriptions and schemas cost roughly 1,377 tokens of context every time the server is loaded.

### Does the agentcheck MCP server require authentication?

No. We connected to agentcheck without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the agentcheck MCP server still maintained?

agentcheck is still listed as active in the MCP registry. We last reached this channel on 25 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://agentwares-agentcheck.vercel.app/api/mcp
- Repository: https://github.com/agentwares/servers
- Website: https://agentwares-agentcheck.vercel.app/
- Changelog RSS feed: https://verifymcp.io/servers/agentwares-agentcheck/api-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/agentwares-agentcheck/api-mcp.json
- HTML version of this page: https://verifymcp.io/servers/agentwares-agentcheck/api-mcp