# 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.

- Trust score: 72/100 (medium)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- remote · `mcp.ai-first.company`: 72/100 (this document), [markdown](https://verifymcp.io/servers/company-ai-first-evidence/mcp.md), [page](https://verifymcp.io/servers/company-ai-first-evidence/mcp)

## Channel facts

- Endpoint: `https://mcp.ai-first.company/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.3.6`

## 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 2026-11-05.

- **Endpoint Security**: 63/100
  - 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.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 74/100
  - 88% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - 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.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 37/100
  - Stability observed for 11 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 10 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 12 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 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.

### Claude

```bash
claude mcp add --transport http company-ai-first-evidence 'https://mcp.ai-first.company/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "company-ai-first-evidence": {
      "url": "https://mcp.ai-first.company/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "company-ai-first-evidence": {
      "type": "http",
      "url": "https://mcp.ai-first.company/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.company-ai-first-evidence]
url = "https://mcp.ai-first.company/mcp"
```

### opencode

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

### OpenClaw

```bash
openclaw mcp add company-ai-first-evidence --url 'https://mcp.ai-first.company/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  company-ai-first-evidence:
    url: "https://mcp.ai-first.company/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "company-ai-first-evidence": {
      "Transport": "http",
      "Url": "https://mcp.ai-first.company/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add company-ai-first-evidence -t streamable-http -u 'https://mcp.ai-first.company/mcp'
```

### Other

```json
{
  "mcpServers": {
    "company-ai-first-evidence": {
      "type": "http",
      "url": "https://mcp.ai-first.company/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.

### 2026-11-05 (score 72, +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.

### 2026-11-03 (score 71, +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.

### 2026-11-01 (score 70, +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.

### 2026-10-30 (score 69, +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.

### 2026-10-27 (score 68, +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.

### 2026-10-26 (score 67, 0)

- [functional improvement] Stability: unverified → 0.03

### 2026-10-25 (score 67)

First indexed and scored.

## MCP tools (10)

### `start` (~115 tokens)

Start here: the map of the evidence

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.

### `open` (~443 tokens)

Open one part of the evidence map

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.

Input parameters:

- `employees` (integer): Number of employees, e.g. 120.
- `free_only` (boolean): Assessments only: free to use.
- `industry` (string): 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.
- `kind` (string): Figures: the kind of number, e.g. measured-in-a-study or self-reported. Assessments: the kind key from start, e.g. readiness-surveys.
- `line` (string): Figures only: the group key from start, e.g. time-freed or running-cost.
- `list` (string, required): Which list: practices, cases, studies, figures, claim-checks or assessments.
- `outcome` (string): Cases with this outcome: measured, company-reported, went-wrong or walked-back.
- `priority` (string): Practices with this priority.
- `size` (string): Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
- `stage` (string): How far the company is: explore, adopt, redesign or run.
- `task` (string): 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.
- `theme` (string): A theme key from start, e.g. governance-and-risk or people-and-skills.

### `get_evidence` (~139 tokens)

Get full evidence records by id

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.

Input parameters:

- `ids` (array, required): Ids as the menus and lists give them, e.g. ["case:klarna-ai-customer-service", "practice:ask-ai-vendors-for-proof"].

### `check_ai_figure` (~112 tokens)

Check an AI statistic before quoting it

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.

### `check_vendor_claim` (~120 tokens)

Check a vendor’s AI 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.

### `review_ai_transformation_plan` (~259 tokens)

Review an AI plan or business case

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.

Input parameters:

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

### `find_practices` (~222 tokens)

Find rated practices for adopting AI

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.

Input parameters:

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

### `find_company_cases` (~302 tokens)

Find company cases of AI use

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.

Input parameters:

- `employees` (integer): Number of employees, e.g. 120.
- `industry` (string): 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.
- `outcome` (string): Cases with this outcome: measured, company-reported, went-wrong or walked-back.
- `size` (string): Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
- `task` (string): The kind of work AI was used for: a task key from start, e.g. customer-service-and-support.
- `theme` (string): A theme key from start, e.g. governance-and-risk or people-and-skills.

### `find_readiness_assessment` (~131 tokens)

Find an AI readiness or literacy 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.

Input parameters:

- `free_only` (boolean): Only assessments that are free to use.
- `kind` (string): The kind key from start, e.g. readiness-surveys or ai-knowledge-tests-and-self-ratings.

### `board_brief` (~266 tokens)

Prepare a board brief on AI adoption

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.

Input parameters:

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

## Diagnostics

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

## Score history

- 2026-11-05: 72
- 2026-11-04: 71
- 2026-11-03: 71
- 2026-11-02: 70
- 2026-11-01: 70
- 2026-10-31: 69
- 2026-10-30: 69
- 2026-10-29: 68
- 2026-10-28: 68
- 2026-10-27: 68
- 2026-10-26: 67
- 2026-10-25: 67

## 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.

## Links

- Remote endpoint: https://mcp.ai-first.company/mcp
- Website: https://mcp.ai-first.company/
- Changelog RSS feed: https://verifymcp.io/servers/company-ai-first-evidence/mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/company-ai-first-evidence/mcp.json
- HTML version of this page: https://verifymcp.io/servers/company-ai-first-evidence/mcp