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AI-first company: evidence on AI adoption

REMOTE · MCP.AI-FIRST.COMPANY

Rated evidence on AI adoption: practices, company cases, claim checks and readiness assessments.

+4 this week 72 Trust /100

Last scanned (8 hours ago)

Scores higher than 58% of remote servers

Reachable. Last reached 5 Nov 2026.

Category AI and Machine Learning

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 Security63
Transport & Reachability100
Schema Quality & AI Usability74
  • 88% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Partial
  • AI-judged instruction clarity (good).Pass
  • Context-footprint check failed: tool/resource definitions use about 2503 tokens (~147/item across 17 items; 10 tools + 7 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 Management37
  • Stability observed for 11 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 10 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
  • An AI judge read all 12 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
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.

  • 5 Nov 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

  • 3 Nov 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.

  • 1 Nov 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.

  • 30 Oct 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.

  • 27 Oct 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.

  • 26 Oct 26 0
    • Stability: unverified → 0.03 ▲ functional
  • 25 Oct 26 67

    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 5 Nov 2026 · Probed https://mcp.ai-first.company/mcp

TLS valid

Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .

Subject Issuer Valid from Valid until Key Signature Serial
CN=mcp.ai-first.company CN=Cloudflare TLS Issuing ECC CA 3,O=SSL Corporation,C=US 17 Oct 2026 15 Jan 2027 ECDSA 256 ECDSA-SHA256 1ef0ddf8d345243a3d8779b4774ee89a
SANs: mcp.ai-first.company
CN=Cloudflare TLS Issuing ECC CA 3,O=SSL Corporation,C=US (CA) CN=SSL.com TLS Transit ECC CA R2,O=SSL Corporation,C=US 29 May 2025 27 May 2035 ECDSA 256 ECDSA-SHA384 31eee88afb87cd9ef8336604743f9b27
CN=SSL.com TLS Transit ECC CA R2,O=SSL Corporation,C=US (CA) CN=SSL.com TLS ECC Root CA 2022,O=SSL Corporation,C=US 21 Oct 2022 17 Oct 2037 ECDSA 384 ECDSA-SHA384 604d8af8d00ba8748b955853172c5f2e
CN=SSL.com TLS ECC Root CA 2022,O=SSL Corporation,C=US (CA) CN=AAA Certificate Services,O=Comodo CA Limited,L=Salford,ST=Greater Manchester,C=GB 1 Aug 2025 31 Dec 2028 ECDSA 384 SHA256-RSA 173dca60ea9f8e1026dafcd72810e48e

Background: What to check on a remote MCP endpoint →

DNSSEC insecure

Validation of mcp.ai-first.company. — Not signed

Zone DS Keys Algorithms Outcome
. trust_anchor 20326, 38696 8, 8 Verified
company. present 6041 8 Verified
ai-first.company. absent Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation
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

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

Transports 2 probes
Transport URL Outcome Status Location
streamable-http https://mcp.ai-first.company/mcp Verified 200
http (plaintext) http://mcp.ai-first.company/mcp Inconclusive 405
MCP tools · 10 exposed · ~2,109 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
board_brief ~266

Prepare a one-page board brief on a question about AI adoption, from rated evidence only. Returns the company cases by the kind of work AI was used for, the themes (with practice counts for the company’s size), the claim checks and figure groups, and how to write the brief: open the cases for the work in question and one to three themes, fetch eight to twelve records and write from them. Use it when someone needs a summary for a board, an owner or an investment committee. To review a written plan, use review_ai_transformation_plan.

NameTypeReqDescription
employeesinteger–Number of employees, e.g. 120.
industrystring–The site’s industry category, if one fits: professional-services (consulting, accounting, audit, law), agencies (marketing, advertising, creative), ecommerce-retail (online shops, retail, wholesale),…
sizestring–Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.

No output schema declared.

No examples provided.

check_ai_figure ~112

Before quoting an AI statistic (e.g. '95% of AI pilots fail', 'only 13% of companies are ready for AI'), check where it comes from, who was asked and what it covers. Returns every claim check of a widely repeated statistic and the groups of other figures; fetch the matching claim check, or open the group the figure belongs to. Use it for figures from surveys, reports and the press. For a claim a vendor makes about its own product, use check_vendor_claim.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

check_vendor_claim ~120

Check a claim made by an AI vendor or consultant, such as '30% productivity gain', 'cuts support costs by 40%' or 'our agents replace 700 staff'. Returns the questions to ask the vendor, the claim checks, the groups of figures and the company cases by the kind of work AI was used for, so you can find the same result and companies that tried the same kind of tool. Use it whenever someone selling or proposing AI quotes a result. For a statistic from the press or a report, use check_ai_figure.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

find_company_cases ~302

List real companies that used AI and how it turned out: measured result, company-reported result, went wrong, or partly walked back. Use it for 'Who has done this?' or 'What went wrong for others?'. Filter by the kind of work AI was used for (task), outcome, theme, industry or company size; returns the cases in one line each, or a menu with counts when there are many. Fetch the ones you use with get_evidence for the story, who produced the figures and the public sources.

NameTypeReqDescription
employeesinteger–Number of employees, e.g. 120.
industrystring–Cases in this industry: professional-services, agencies, ecommerce-retail, manufacturing, or other (none of the four, e.g. banks, airlines, public bodies). Practices apply to all industries.
outcomestring–Cases with this outcome: measured, company-reported, went-wrong or walked-back.
sizestring–Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
taskstring–The kind of work AI was used for: a task key from start, e.g. customer-service-and-support.
themestring–A theme key from start, e.g. governance-and-risk or people-and-skills.

No output schema declared.

No examples provided.

find_practices ~222

List practices for adopting AI that fit a company's size, rated by strength of evidence. Use it for 'Where should we start with AI?' or 'What should a 120-person manufacturer do first?'. Pass the number of employees or a size band, and optionally the theme, stage or priority. Returns the practices in one line each, or a menu by theme when there are many; fetch the ones you use with get_evidence for their studies, cases and links.

NameTypeReqDescription
employeesinteger–Number of employees, e.g. 120.
prioritystring–Practices with this priority.
sizestring–Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
stagestring–How far the company is: explore, adopt, redesign or run.
themestring–A theme key from start, e.g. governance-and-risk or people-and-skills.

No output schema declared.

No examples provided.

find_readiness_assessment ~131

List published questionnaires, tests and task maps to assess AI readiness, AI literacy, staff attitudes or which tasks suit AI, best tested first, optionally by kind or free to use. Fetch the ones that fit with get_evidence for how well each has been tested, who answers it, its length, licence and cost. For advice on what to do, use find_practices.

NameTypeReqDescription
free_onlyboolean–Only assessments that are free to use.
kindstring–The kind key from start, e.g. readiness-surveys or ai-knowledge-tests-and-self-ratings.

No output schema declared.

No examples provided.

get_evidence ~139

Get the full records for ids from the menus and lists (such as "case:klarna-ai-customer-service" or "claim-check:mit-95-percent-of-ai-pilots-fail"): figures, ratings, verdicts, caveats, primary sources, the ai-first.company link, and the ids of related studies, cases and practices to follow. Up to 20 ids per call. Quote and cite only from what this returns.

NameTypeReqDescription
idsarrayyesIds as the menus and lists give them, e.g. ["case:klarna-ai-customer-service", "practice:ask-ai-vendors-for-proof"].

No output schema declared.

No examples provided.

open ~443

Open one part of the evidence map: a list (practices, cases, studies, figures, claim-checks, assessments) with filters such as theme, outcome, industry, company size, stage, priority, figure group (line) or assessment kind. Returns every matching row in one line with its id, or, when there are more than 25, a menu splitting them by the next filter, with counts. Use the arguments the menus show. Then fetch the rows you pick with get_evidence.

NameTypeReqDescription
employeesinteger–Number of employees, e.g. 120.
free_onlyboolean–Assessments only: free to use.
industrystring–Cases in this industry: professional-services, agencies, ecommerce-retail, manufacturing, or other (none of the four, e.g. banks, airlines, public bodies). Practices apply to all industries.
kindstring–Figures: the kind of number, e.g. measured-in-a-study or self-reported. Assessments: the kind key from start, e.g. readiness-surveys.
linestring–Figures only: the group key from start, e.g. time-freed or running-cost.
liststringyesWhich list: practices, cases, studies, figures, claim-checks or assessments.
outcomestring–Cases with this outcome: measured, company-reported, went-wrong or walked-back.
prioritystring–Practices with this priority.
sizestring–Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
stagestring–How far the company is: explore, adopt, redesign or run.
taskstring–Cases only: the kind of work AI was used for, a task key from start, e.g. customer-service-and-support or legal-research-drafting-and-filings.
themestring–A theme key from start, e.g. governance-and-risk or people-and-skills.

No output schema declared.

No examples provided.

review_ai_transformation_plan ~259

Review an AI transformation proposal, business case or rollout memo, for example before it goes to a board. Returns the do-first practices for the company’s size as a checklist, each with its evidence rating and wording that may show it, and how to find similar cases. You read the plan and decide for each practice whether the plan commits to it, rules it out or leaves it out. Pass the size and industry; the plan itself stays with you. For one figure in the plan, use check_ai_figure.

NameTypeReqDescription
employeesinteger–Number of employees, e.g. 120.
industrystring–The site’s industry category, if one fits: professional-services (consulting, accounting, audit, law), agencies (marketing, advertising, creative), ecommerce-retail (online shops, retail, wholesale),…
sizestring–Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.

No output schema declared.

No examples provided.

start ~115

Start here for any question about adopting AI in a company: the map of the ai-first.company evidence. Returns the company cases by the kind of work AI was used for (customer service, legal work, office assistant and others), the six themes (with counts of practices, cases and studies), the claim checks of widely repeated AI statistics, the groups of other figures, the company cases by outcome, the assessments by kind, and what each filter value and rating means. Then open the part that fits and fetch records with get_evidence.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

Install

How do I install the AI-first company: evidence on AI adoption MCP server?

AI-first company: evidence on AI adoption is a hosted endpoint at https://mcp.ai-first.company/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 · mcp.ai-first.company

# add to Claude Code
claude mcp add --transport http company-ai-first-evidence 'https://mcp.ai-first.company/mcp'
Common questions

What is the AI-first company: evidence on AI adoption MCP server?

AI-first company: evidence on AI adoption is an MCP server listed in the public MCP registry as company.ai-first/evidence. Rated evidence on AI adoption: practices, company cases, claim checks and readiness assessments. This page covers its hosted endpoint (https://mcp.ai-first.company/mcp).

Is the AI-first company: evidence on AI adoption MCP server safe to use?

AI-first company: evidence on AI adoption scores 72 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 AI-first company: evidence on AI adoption MCP server expose?

AI-first company: evidence on AI adoption exposes 10 tools: start, open, get_evidence, check_ai_figure, check_vendor_claim, and 5 more. Their descriptions and schemas cost roughly 2,109 tokens of context every time the server is loaded.

Does the AI-first company: evidence on AI adoption MCP server require authentication?

No. We connected to AI-first company: evidence on AI adoption without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

Is the AI-first company: evidence on AI adoption MCP server still maintained?

AI-first company: evidence on AI adoption is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.