Skip to content
verify mcp Beta VerifyMCP is currently in beta. If you notice any issues, get in touch and we’ll put it right.

zRev AI

REMOTE · GO.ZREV.AI · SCANNED SEP 26

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

Available components

+3 this week 74 Trust /100
Trust breakdown (7 categories)

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. How we score → Why this is hard to score →

Endpoint Security71
  • The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
  • No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one. See how to fix → View diagnostics → Partial
  • HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement. View diagnostics → Unverified
  • The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
  • DNSSEC is configured correctly; the domain's records validate against the full chain to the root. View diagnostics → Pass
Transport & Reachability100
Schema Quality & AI Usability73
  • AI-judged instruction clarity (excellent).Pass
  • 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. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management30
  • Stability observed for 9 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
Tool Safety100
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • We read all 9 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
  • An AI judge read all 10 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
  • Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
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.

remote · go.zrev.ai

# add to Claude Code
claude mcp add --transport http ai-zrev-zrev 'https://go.zrev.ai/mcp'
// .cursor/mcp.json
{
  "mcpServers": {
    "ai-zrev-zrev": {
      "url": "https://go.zrev.ai/mcp"
    }
  }
}
// .vscode/mcp.json
{
  "servers": {
    "ai-zrev-zrev": {
      "type": "http",
      "url": "https://go.zrev.ai/mcp"
    }
  }
}
# ~/.codex/config.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
    }
  }
}
# add to OpenClaw
openclaw mcp add ai-zrev-zrev --url 'https://go.zrev.ai/mcp' --transport streamable-http
# ~/.hermes/config.yaml
mcp_servers:
  ai-zrev-zrev:
    url: "https://go.zrev.ai/mcp"
// ~/.netclaw/config/netclaw.json
{
  "McpServers": {
    "ai-zrev-zrev": {
      "Transport": "http",
      "Url": "https://go.zrev.ai/mcp"
    }
  }
}
# add to Vellum
assistant mcp add ai-zrev-zrev -t streamable-http -u 'https://go.zrev.ai/mcp'
// mcp.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 we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 26 Sept 26 +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.

  • 25 Sept 26 0
    • We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
  • 24 Sept 26 +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.

  • 22 Sept 26 +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.

  • 19 Sept 26 +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.

  • 18 Sept 26 +7
    • 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 security
    • Schema quality: pass → fail ▼ functional
    • Stability: unverified → 0.03 ▲ functional
    • Schema quality: good → excellent functional
    • “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 cosmetic
  • 17 Sept 26 63

    First indexed and scored.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 26 Sept 2026 · Probed https://go.zrev.ai/mcp

TLS valid

Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .

Subject Issuer Valid from Valid until Key Signature Serial
CN=zrev.ai CN=WE1,O=Google Trust Services,C=US 18 Aug 2026 16 Nov 2026 ECDSA 256 ECDSA-SHA256 2944f1b90dd8580e0ecf04925445cd53
SANs: zrev.ai, go.zrev.ai, *.go.zrev.ai
CN=WE1,O=Google Trust Services,C=US (CA) CN=GTS Root R4,O=Google Trust Services LLC,C=US 13 Dec 2023 20 Feb 2029 ECDSA 256 ECDSA-SHA384 7ff31977972c224a76155d13b6d685e3
CN=GTS Root R4,O=Google Trust Services LLC,C=US (CA) CN=GlobalSign Root CA,OU=Root CA,O=GlobalSign nv-sa,C=BE 15 Nov 2023 28 Jan 2028 ECDSA 384 SHA256-RSA 7fe530bf331343bedd821610493d8a1b

Background: What to check on a remote MCP endpoint →

DNSSEC secure

Validation of go.zrev.ai. — Secure

Zone DS Keys Algorithms Outcome
. trust_anchor 20326, 38696 8, 8 Verified
ai. present 3799 8 Verified
zrev.ai. present 2371 13 Verified
go.zrev.ai. Verified address RRset verified with the apex keys
Authentication No authorisation required

The endpoint answered without asking for a token. Anyone who knows the URL can reach it.

Result No authorisation required
HTTP status 200
Header Value
strict-transport-security max-age=63072000; includeSubDomains; preload
x-content-type-options nosniff
x-frame-options DENY
referrer-policy strict-origin-when-cross-origin
permissions-policy camera=(), microphone=(), geolocation=(), payment=()

Background: How OAuth 2.1 works in the 2026 MCP spec →

Transports 2 probes
Transport URL Outcome Status Location
streamable-http https://go.zrev.ai/mcp Verified 200
http (plaintext) http://go.zrev.ai/mcp Inconclusive 405
MCP tools · 9 exposed · ~1,404 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
about_zrev ~92

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.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

book_call ~94

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.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

cold_read ~186

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.

NameTypeReqDescription
domainstringyesBare domain to read, without protocol or path, e.g. acme.com

No output schema declared.

No examples provided.

estimate_roi ~265

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.

NameTypeReqDescription
arrnumberyesAnnual recurring revenue in USD, e.g. 10000000
cacnumberyesCustomer acquisition cost per new customer in USD, e.g. 8000
close_rate_pctnumberyesClose rate as a percentage, e.g. 15 for 15%
deal_sizenumberyesAverage deal size in USD, e.g. 25000
monthly_leadsnumberyesQualified leads per month, e.g. 120
team_sizenumberyesNumber of people on the GTM team, account executives plus SDRs, e.g. 8

No output schema declared.

No examples provided.

get_benchmarks ~149

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.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

get_engagement_timeline ~82

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.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

grade_llms_txt ~174

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.

NameTypeReqDescription
domainstringyesBare domain to grade, without protocol or path, e.g. acme.com

No output schema declared.

No examples provided.

gtm_diligence_checklist ~168

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.

NameTypeReqDescription
dimensionstring–Optional. A dimension number from 1 to 10, or a keyword such as 'churn', 'pipeline' or 'marketing'. Omit to list the ten dimensions.

No output schema declared.

No examples provided.

leave_contact ~194

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.

NameTypeReqDescription
companystring–Optional. Their company name
emailstringyesThe human's email address, given by them for this purpose
namestring–Optional. The human's name
notestring–Optional. What they want to talk about, in their words

No output schema declared.

No examples provided.

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.