AI-first company: evidence on AI adoption is answering right now. Last checked 1 min ago. It exposes 10 tools.
Rated evidence on AI adoption: practices, company cases, claim checks and readiness assessments.
Uptime history
5 hours of history
5 hours agonow
100.0%
Uptime 24h
16 of 16 checks
10
Tools
read from the server
362 ms
Response time
average over 24h
open, no key
Access
streamable-http
Nothing serious here today
Today is the operative word: we check AI-first company: evidence on AI adoption every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.
Connect this server
Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 1 min ago.
run in your terminal
claude mcp add evidence --transport http https://mcp.ai-first.company/mcpSettings → Connectors → Add custom connector, then paste this address
https://mcp.ai-first.company/mcp~/.codex/config.toml
[mcp_servers.evidence]
url = "https://mcp.ai-first.company/mcp".cursor/mcp.json
{
"mcpServers": {
"evidence": {
"url": "https://mcp.ai-first.company/mcp"
}
}
}.vscode/mcp.json
{
"servers": {
"evidence": {
"type": "http",
"url": "https://mcp.ai-first.company/mcp"
}
}
}Available tools 10
Read directly from the server with tools/list, grouped by what they act on.
If a tool disappears, we record the date.
board
board_brief
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.
company
find_company_cases
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.
evidence
get_evidence
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.
figure
check_ai_figure
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.
open
open
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.
practices
find_practices
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.
readiness
find_readiness_assessment
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.
review
review_ai_transformation_plan
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.
start
start
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.
vendor
check_vendor_claim
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.
Endpoints
| URL | Transport | State | Latency | Checked |
|---|---|---|---|---|
| https://mcp.ai-first.company/mcp | streamable-http | answering | 314 ms | 1 min ago |
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AI-first company: evidence on AI adoption — questions
Answers built from our own checks of this server.
What can AI-first company: evidence on AI adoption do?
It exposes 10 tools, read directly from the server on our last check. Among them: board_brief, check_ai_figure, check_vendor_claim, find_company_cases, find_practices, find_readiness_assessment and 4 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README.
How MCP servers expose tools in the first place →
Is AI-first company: evidence on AI adoption working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 16 of 16 checks got a reply (100.0%), average response time 362 ms. The bar chart above shows every period we have measured.
How do I connect AI-first company: evidence on AI adoption?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does AI-first company: evidence on AI adoption need an API key?
No. AI-first company: evidence on AI adoption completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 10 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is AI-first company: evidence on AI adoption?
It answers our handshake in 362 ms on average, which is faster than 42% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.