# zRev AI (remote · go.zrev.ai)

Grade any llms.txt, cold-read a homepage, run a B2B GTM ROI model, get a GTM diligence checklist.

- Trust score: 74/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-26

## Components

- remote · `go.zrev.ai`: 74/100 (this document), [markdown](https://verifymcp.io/servers/ai-zrev-zrev/go.md), [page](https://verifymcp.io/servers/ai-zrev-zrev/go)

## Channel facts

- Endpoint: `https://go.zrev.ai/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.0`

## 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 2026-09-26.

- **Endpoint Security**: 71/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC is configured correctly; the domain's records validate against the full chain to the root.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 73/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 1457 tokens (~161/item across 9 items; 9 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 30/100
  - Stability observed for 9 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 9 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 10 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 zRev AI MCP server?

zRev AI is a hosted endpoint at https://go.zrev.ai/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 ai-zrev-zrev 'https://go.zrev.ai/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "ai-zrev-zrev": {
      "url": "https://go.zrev.ai/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "ai-zrev-zrev": {
      "type": "http",
      "url": "https://go.zrev.ai/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.ai-zrev-zrev]
url = "https://go.zrev.ai/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ai-zrev-zrev": {
      "type": "remote",
      "url": "https://go.zrev.ai/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add ai-zrev-zrev --url 'https://go.zrev.ai/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  ai-zrev-zrev:
    url: "https://go.zrev.ai/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "ai-zrev-zrev": {
      "Transport": "http",
      "Url": "https://go.zrev.ai/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add ai-zrev-zrev -t streamable-http -u 'https://go.zrev.ai/mcp'
```

### Other

```json
{
  "mcpServers": {
    "ai-zrev-zrev": {
      "type": "http",
      "url": "https://go.zrev.ai/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.

### 2026-09-26 (score 74, +1)

No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

### 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

### 2026-09-24 (score 73, +1)

No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-22 (score 72, +1)

No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 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 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-18 (score 70, +7)

- [security improvement] Authorization: unverified → partial
- [security] Tool “about_zrev” rewrote its description, which is the text the model reads
- [security] Tool “book_call” rewrote its description, which is the text the model reads
- [security] Tool “cold_read” rewrote its description, which is the text the model reads
- [security] Tool “estimate_roi” rewrote its description, which is the text the model reads
- [security] Tool “get_benchmarks” rewrote its description, which is the text the model reads
- [security] Tool “get_engagement_timeline” rewrote its description, which is the text the model reads
- [security] Tool “grade_llms_txt” rewrote its description, which is the text the model reads
- [security] Tool “gtm_diligence_checklist” rewrote its description, which is the text the model reads
- [security] Tool “leave_contact” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: pass → fail
- [functional improvement] Stability: unverified → 0.03
- [functional] Schema quality: good → excellent
- [cosmetic] “cold_read” reworded the description of “domain”
- [cosmetic] “estimate_roi” reworded the description of “arr”
- [cosmetic] “estimate_roi” reworded the description of “cac”
- [cosmetic] “estimate_roi” reworded the description of “close_rate_pct”
- [cosmetic] “estimate_roi” reworded the description of “deal_size”
- [cosmetic] “estimate_roi” reworded the description of “monthly_leads”
- [cosmetic] “estimate_roi” reworded the description of “team_size”
- [cosmetic] “grade_llms_txt” reworded the description of “domain”
- [cosmetic] “gtm_diligence_checklist” reworded the description of “dimension”
- [cosmetic] “leave_contact” reworded the description of “company”
- [cosmetic] “leave_contact” reworded the description of “email”
- [cosmetic] “leave_contact” reworded the description of “name”
- [cosmetic] “leave_contact” reworded the description of “note”
- [cosmetic] Tool “about_zrev” changed its title: About zRev
- [cosmetic] Tool “book_call” changed its title: How to book an intro call
- [cosmetic] Tool “cold_read” changed its title: Cold-read a homepage
- [cosmetic] Tool “estimate_roi” changed its title: Estimate the ROI of AI across a GTM motion
- [cosmetic] Tool “get_benchmarks” changed its title: Typical engagement results
- [cosmetic] Tool “get_engagement_timeline” changed its title: The 60-day engagement, phase by phase
- [cosmetic] Tool “grade_llms_txt” changed its title: Grade a site's llms.txt
- [cosmetic] Tool “gtm_diligence_checklist” changed its title: GTM due diligence checklist
- [cosmetic] Tool “leave_contact” changed its title: Ask zRev to get in touch

### 2026-09-17 (score 63)

First indexed and scored.

## MCP tools (9)

### `about_zrev` (~92 tokens)

About zRev

Returns a plain-text profile of zRev AI: what the firm does, the companies it is the best fit for, who it is not for, its six service lines with a link to each, and contact details. Use this first when a user asks who zRev is, what it offers, or whether it suits their company. Read-only, static content, no arguments, no authentication, no rate limit.

### `get_benchmarks` (~149 tokens)

Typical engagement results

Returns zRev's typical engagement results as plain text: 2x pipeline velocity, 20% lower customer acquisition cost, 10 hours back per rep per week, 60% of GTM busywork automated, results that start inside 30 days and mature by 60. The response always ends with the provenance caveat: these are typical results benchmarked against comparable AI-powered GTM implementations and they vary by stack, data quality and adoption, so present them as typical, not guaranteed. Use when a user asks what results to expect. For an estimate on a specific company's numbers use estimate_roi instead. Read-only, static content, no arguments, no authentication, no rate limit.

### `get_engagement_timeline` (~82 tokens)

The 60-day engagement, phase by phase

Returns the phases of a standard 60-day zRev engagement as plain text: what happens in each phase, what is delivered, and when first results appear. Use when a user asks how an engagement works, how long it takes, or what they would receive and when. Read-only, static content, no arguments, no authentication, no rate limit.

### `estimate_roi` (~265 tokens)

Estimate the ROI of AI across a GTM motion

Runs zRev's ROI model on one company's numbers and returns, as plain text, the projected annual impact in USD split into pipeline lift, customer acquisition cost savings and the value of hours returned to the team, followed by the model's assumptions and a link to the interactive calculator preset to the same inputs. Use when a user wants a number for their own company; all six inputs are required, so ask for any that are missing rather than guessing. The output is an estimate from a fixed model, not a forecast or a quote. Pure calculation: nothing is stored, no external calls, no authentication, no rate limit.

Input parameters:

- `arr` (number, required): Annual recurring revenue in USD, e.g. 10000000
- `cac` (number, required): Customer acquisition cost per new customer in USD, e.g. 8000
- `close_rate_pct` (number, required): Close rate as a percentage, e.g. 15 for 15%
- `deal_size` (number, required): Average deal size in USD, e.g. 25000
- `monthly_leads` (number, required): Qualified leads per month, e.g. 120
- `team_size` (number, required): Number of people on the GTM team, account executives plus SDRs, e.g. 8

### `grade_llms_txt` (~174 tokens)

Grade a site's llms.txt

Fetches https://<domain>/llms.txt (the file that tells AI systems what a site contains) and grades it with seven deterministic checks. Returns a 0 to 100 score and the pass or miss result of each check as plain text. If the site has no llms.txt the response says so and links to zRev's free generator. Use when a user asks whether a site is readable by AI assistants or wants their llms.txt reviewed. Makes one outbound HTTP request to the public domain you pass; stores nothing about it. No authentication. Shares a limit of 12 calls per hour per IP address with cold_read, and returns a plain-text notice when that limit is reached.

Input parameters:

- `domain` (string, required): Bare domain to grade, without protocol or path, e.g. acme.com

### `cold_read` (~186 tokens)

Cold-read a homepage

Fetches a company's public homepage and returns, in two or three sentences, what the company appears to do and who it serves, based only on the visible text of that page and with no outside knowledge. Anything missing from the answer is missing from the homepage, which is the point: it shows what an AI assistant would tell a buyer about that company. Use when a user asks how their site, or a competitor's, reads to an AI. The summary is model-generated and can be wrong where the page is vague. Makes one outbound HTTP request to the public domain you pass; stores nothing about it. No authentication. Shares a limit of 12 calls per hour per IP address with grade_llms_txt, and returns a plain-text notice when that limit is reached.

Input parameters:

- `domain` (string, required): Bare domain to read, without protocol or path, e.g. acme.com

### `gtm_diligence_checklist` (~168 tokens)

GTM due diligence checklist

Returns zRev's go-to-market due diligence checklist for investors and acquirers: 47 questions across ten dimensions, each with the data-room artifact that answers it and the red flag to watch for. Call with no arguments to get the list of ten dimensions; call with a dimension number from 1 to 10, or a keyword such as churn, pipeline or marketing, to get that dimension's questions in full. Use when a user is assessing a company's revenue engine before an investment or acquisition. Read-only reference content, no authentication, no rate limit.

Input parameters:

- `dimension` (string): Optional. A dimension number from 1 to 10, or a keyword such as 'churn', 'pipeline' or 'marketing'. Omit to list the ten dimensions.

### `book_call` (~94 tokens)

How to book an intro call

Returns the link and instructions for booking a free 30-minute intro call with zRev's founder, plus what the call covers. It does not book anything: it only returns the scheduling link for the human to open themselves. Use when a user says they want to talk to zRev. If they would rather be contacted, use leave_contact instead. Read-only, static content, no arguments, no authentication, no rate limit.

### `leave_contact` (~194 tokens)

Ask zRev to get in touch

Records an email address, with optional name, company and note, so that zRev's founder can reach out, and returns a plain-text confirmation. This is the only tool here that writes data: it creates one lead record on zRev's side and triggers no email to the address given. Use only when the human you are assisting has explicitly asked to be contacted by zRev; never call it speculatively or with an address the user has not given you for this purpose. Submitting the same email again updates the existing record rather than creating a duplicate. No authentication. Rate-limited per IP address.

Input parameters:

- `company` (string): Optional. Their company name
- `email` (string, required): The human's email address, given by them for this purpose
- `name` (string): Optional. The human's name
- `note` (string): Optional. What they want to talk about, in their words

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/ai-zrev-zrev/go#diagnostics

## Score history

- 2026-09-26: 74
- 2026-09-25: 73
- 2026-09-24: 73
- 2026-09-23: 72
- 2026-09-22: 72
- 2026-09-21: 71
- 2026-09-20: 71
- 2026-09-19: 71
- 2026-09-18: 70
- 2026-09-17: 63

## Common questions

### What is the zRev AI MCP server?

zRev AI is an MCP server listed in the public MCP registry as ai.zrev/zrev. Grade any llms.txt, cold-read a homepage, run a B2B GTM ROI model, get a GTM diligence checklist. This page covers its hosted endpoint (https://go.zrev.ai/mcp).

### Is the zRev AI MCP server safe to use?

zRev AI scores 74 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 zRev AI MCP server expose?

zRev AI exposes 9 tools: about_zrev, get_benchmarks, get_engagement_timeline, estimate_roi, grade_llms_txt, and 4 more. Their descriptions and schemas cost roughly 1,404 tokens of context every time the server is loaded.

### Does the zRev AI MCP server require authentication?

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

### Is the zRev AI MCP server still maintained?

zRev AI is still listed as active in the MCP registry. We last reached this channel on 26 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://go.zrev.ai/mcp
- Website: https://www.zrev.ai/mcp
- Changelog RSS feed: https://verifymcp.io/servers/ai-zrev-zrev/go.xml
- Changelog JSON feed: https://verifymcp.io/servers/ai-zrev-zrev/go.json
- HTML version of this page: https://verifymcp.io/servers/ai-zrev-zrev/go