Name: AI-first company: evidence on AI adoption
Author: ai-first

AI-first company: evidence on AI adoption MCP

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

Tool-list grade A Remote · Streamable HTTP No auth needed 10 tools v0.3.6

No reviews yet by ai-first The publisher’s own endpoint — this marketplace is not in the path

96/100

tool-list score

tools declared

remote

at its publisher

—

calls last 7d

What it does

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

Quickstart

# 1 — add it to Claude Code
claude mcp add --scope user --transport http ai-first-company-evidence-on-ai-adoption-mcp https://mcp.ai-first.company/mcp

# 2 — ask your agent for something these tools do
#   start, open, get_evidence and 7 more

AI-first company: evidence on AI adoption costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.

Publishernot claimed

ai-first1 server

1 category

All 1 server

Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.

Tool surface

What the model actually sees. Descriptions are diffed on every release — see version history.

Only tools that write or reach the network

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

No parameter schema on file.

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. Th

Parameter	Type	Required	Notes
list	string	yes	Which list: practices, cases, studies, figures, claim-checks or assessments.
theme	string	no	A theme key from start, e.g. governance-and-risk or people-and-skills.
task	string	no	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.
line	string	no	Figures only: the group key from start, e.g. time-freed or running-cost.
kind	string	no	Figures: the kind of number, e.g. measured-in-a-study or self-reported. Assessments: the kind key from start, e.g. readiness-surveys.
outcome	string	no	Cases with this outcome: measured, company-reported, went-wrong or walked-back.
industry	string	no	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.
size	string	no	Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
employees	integer	no	Number of employees, e.g. 120.
stage	string	no	How far the company is: explore, adopt, redesign or run.
priority	string	no	Practices with this priority.
free_only	boolean	no	Assessments only: free to use.

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.

Parameter	Type	Required	Notes
ids	array	yes	Ids as the menus and lists give them, e.g. ["case:klarna-ai-customer-service", "practice:ask-ai-vendors-for-proof"].

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 v

No parameter schema on file.

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

No parameter schema on file.

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 ind

Parameter	Type	Required	Notes
size	string	no	Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
employees	integer	no	Number of employees, e.g. 120.
industry	string	no	The site’s industry category, if one fits: professional-services (consulting, accounting, audit, law), agencies (marketing, advertising, creative), ecommerce-retail (online shops, retail, wholesale),

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 fo

Parameter	Type	Required	Notes
size	string	no	Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
employees	integer	no	Number of employees, e.g. 120.
theme	string	no	A theme key from start, e.g. governance-and-risk or people-and-skills.
stage	string	no	How far the company is: explore, adopt, redesign or run.
priority	string	no	Practices with this priority.

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 g

Parameter	Type	Required	Notes
task	string	no	The kind of work AI was used for: a task key from start, e.g. customer-service-and-support.
outcome	string	no	Cases with this outcome: measured, company-reported, went-wrong or walked-back.
theme	string	no	A theme key from start, e.g. governance-and-risk or people-and-skills.
industry	string	no	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.
size	string	no	Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
employees	integer	no	Number of employees, e.g. 120.

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.

Parameter	Type	Required	Notes
kind	string	no	The kind key from start, e.g. readiness-surveys or ai-knowledge-tests-and-self-ratings.
free_only	boolean	no	Only assessments that are free to use.

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 wh

Parameter	Type	Required	Notes
size	string	no	Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
employees	integer	no	Number of employees, e.g. 120.
industry	string	no	The site’s industry category, if one fits: professional-services (consulting, accounting, audit, law), agencies (marketing, advertising, creative), ecommerce-retail (online shops, retail, wholesale),

Free

$0 forever

Every tool — any call limit is its publisher’s, not ours
No card, and mcprush charges nothing for it
Runs at its publisher’s own address — this marketplace is not in the path
Nothing to cap, because mcprush bills nothing

Nothing here is billable: mcprush charges nothing to install AI-first company: evidence on AI adoption or to call it, at any volume.

ATool-list grade · score 96 / 100

Release 0.3.6 · scanned 28 Oct 2026

Graded from the tools this server lists, not from its source code.

Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.

Threat score100

Client score96

Findings0 threat · 0 capability

Capabilityminimal

Coveragemanifest

Verificationnot in this report

Surface digest7c05d5659970ce6eae1e550bef4681ccac6ce193c41803226f7928c505f22af1

Score vectorthreat 100 → client 96

Term	Level	What it prices	Points
capability-exposure	minimal	capability blast radius (minimal) — client exposure if the model is manipulated	−0
coverage-honesty	manifest	inspection depth (manifest) — how much of the target the scan could see	−4

score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.

What the scan could actually read

A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.

The scan read each tool’s name, description, input schema and annotations exactly as the server listed them the last time it answered us; no code was reviewed. We ask the server again about once a month, and a list that changed is graded again.

Tool surfaceread

Implementation sourcenot available

Package metadatanot available

Live transportnot available

The implementation source was not read — this reflects the declared tool metadata only.

Capability — what it could do if the model were manipulated

Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.

Tool	Capability tags	Why the tag was assigned
start	untrusted-input	untrusted-input: keyword "fetch"
open	untrusted-input	untrusted-input: keyword "fetch"
get_evidence	no tags
check_ai_figure	untrusted-input	untrusted-input: keyword "fetch"
check_vendor_claim	no tags
review_ai_transformation_plan	no tags
find_practices	untrusted-input	untrusted-input: keyword "fetch"
find_company_cases	untrusted-input	untrusted-input: keyword "fetch"
find_readiness_assessment	untrusted-input	untrusted-input: keyword "fetch"
board_brief	untrusted-input	untrusted-input: keyword "fetch"

Toxic-flow graph

The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.

The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.

Supply chain and provenance

Integrityfirst-seen

Surface digest7c05d5659970ce6e

Scanned2026-10-28

This is the first scan of this surface here, so there is nothing yet to compare it against.

Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.

Where are you running it?

Tool surface

Release history

AI-first company: evidence on AI adoption MCP

What it does

Quickstart

Where are you running it?

Tool surface

What counts against your monthly calls

What the scan could actually read

Capability — what it could do if the model were manipulated

Toxic-flow graph

Supply chain and provenance

Release history

See also