# OI AI Adoption (remote · operative-intelligence.com)

Advice on bringing AI into work processes, with sourced effects, risks and rules for SI, HR, DE, AT

- Trust score: 80/100 (high trust)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- remote · `operative-intelligence.com`: 80/100 (this document), [markdown](https://verifymcp.io/servers/com-operative-intelligence-ai-adoption/ai-adoption.md), [page](https://verifymcp.io/servers/com-operative-intelligence-ai-adoption/ai-adoption)

## Channel facts

- Endpoint: `https://operative-intelligence.com/ai-adoption`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.1.0`

## 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**: 74/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Nothing on it is marked destructive, but we can't show that an anonymous caller can only read: full credit needs the server to list at least one tool and every tool to be verifiably read-only. We use a best-effort check of tool names and descriptions to tell read-only tools from tools that change things, so some genuinely read-only tools may not get full credit. Names that start with a read verb (get, list, read, search and similar) and accurate readOnlyHint annotations help. Meet that to earn full credit, or require authorisation.
  - HTTPS is enforced; there's no plaintext access path.
  - 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**: 73/100
  - 100% 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 2232 tokens (~279/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 70/100
  - Stability observed for 21 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 91/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 73% 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 8 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 9 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### How do I install the OI AI Adoption MCP server?

OI AI Adoption is a hosted endpoint at https://operative-intelligence.com/ai-adoption, 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 com-operative-intelligence-ai-adoption 'https://operative-intelligence.com/ai-adoption'
```

### Cursor

```json
{
  "mcpServers": {
    "com-operative-intelligence-ai-adoption": {
      "url": "https://operative-intelligence.com/ai-adoption"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "com-operative-intelligence-ai-adoption": {
      "type": "http",
      "url": "https://operative-intelligence.com/ai-adoption"
    }
  }
}
```

### Codex

```toml
[mcp_servers.com-operative-intelligence-ai-adoption]
url = "https://operative-intelligence.com/ai-adoption"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-operative-intelligence-ai-adoption": {
      "type": "remote",
      "url": "https://operative-intelligence.com/ai-adoption",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-operative-intelligence-ai-adoption --url 'https://operative-intelligence.com/ai-adoption' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  com-operative-intelligence-ai-adoption:
    url: "https://operative-intelligence.com/ai-adoption"
```

### Netclaw

```json
{
  "McpServers": {
    "com-operative-intelligence-ai-adoption": {
      "Transport": "http",
      "Url": "https://operative-intelligence.com/ai-adoption"
    }
  }
}
```

### Vellum

```bash
assistant mcp add com-operative-intelligence-ai-adoption -t streamable-http -u 'https://operative-intelligence.com/ai-adoption'
```

### Other

```json
{
  "mcpServers": {
    "com-operative-intelligence-ai-adoption": {
      "type": "http",
      "url": "https://operative-intelligence.com/ai-adoption"
    }
  }
}
```

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 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 67 to 70. That category is still filling its 30-day observation window: 20 days of observed history at the previous scan, 21 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-03 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-01 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-29 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-27 (score 76, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-25 (score 75, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-23 (score 74, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-21 (score 73, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (8)

### `get_readiness_questions` (~90 tokens)

Get readiness questions

Returns the self-assessment questions on AI readiness in four areas (data, processes, people, compliance), each with its id, the reason it is asked and the allowed answer options. Answers are option ids, not free text.

Input parameters:

- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.

### `assess_ai_readiness` (~124 tokens)

Assess AI readiness

Scores the answers from get_readiness_questions with fixed rules: a level from 0 to 4 per area, the rule behind each limit, the gaps found and a next step for each gap. Unanswered areas are reported as not assessed. The result is a self-assessment.

Input parameters:

- `answers` (object, required): Answers keyed by question id, each value an option id from get_readiness_questions
- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.

### `estimate_ai_costs` (~371 tokens)

Estimate AI costs and payback

Estimates the costs of introducing AI, shown separately as external costs (contractor, DPIA, legal review, AI literacy training), internal staff time, licences per month and operations per month (hosting, model usage, maintenance), and the payback period in three savings scenarios. Public data with source and retrieval date: Eurostat labour costs (for internal staff time only), published licence, hosting and model price lists, the ECB exchange rate, and savings effects from four published studies. Amounts without a reliable public source are inputs; when they are missing, the result lists them and does not assume values. Also names, per country (SI, AT, DE, HR), where to check current public funding calls for digitalisation and AI; it gives no amounts and does not say whether any call is open.

Input parameters:

- `compliance` (object)
- `country` (string): ISO country code: SI, AT, DE or HR; otherwise the EU average
- `external` (object)
- `hosting` (object)
- `internal_hourly_cost_eur` (number)
- `internal_hours` (object)
- `internal_it_hourly_cost_eur` (number)
- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.
- `licence` (object)
- `maintenance_eur_per_month` (number)
- `model_usage` (object)
- `own_savings_share` (number): Own estimate of the share of process time saved; replaces the study
- `process_hours_per_week` (number, required): Hours per week currently spent on the process
- `task_type` (string): Type of work, to apply the effect measured in a published study

### `plan_ai_rollout` (~133 tokens)

Plan AI rollout

Returns a phased rollout plan (discovery, pilot, rollout, operations) with tasks, roles in RACI form, risks and indicators for the chosen implementation pattern. When readiness answers are given, preparation tasks for areas at level 0 or 1 are added to the discovery phase.

Input parameters:

- `answers` (object): Answers keyed by question id, each value an option id from get_readiness_questions
- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.
- `pattern` (string, required): Implementation pattern

### `adoption_list_process_types` (~81 tokens)

List process types

Returns the six process types of the knowledge base (data capture, communication, documents, planning, control, knowledge) with a definition and the ids of the adoption patterns of each type.

Input parameters:

- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.

### `adoption_find_patterns` (~685 tokens)

Advise on AI for a described process

Advises on using AI in a process the user describes, from a curated knowledge base with sources. The caller passes the description, the task that best matches it and the process properties it states; the server selects deterministically and does not use a language model. The result puts questions to ask first (ask_first) at the top, then one recommended way as a workflow (recommendation: how it would work, what it brings with sourced effects only, what is needed, what to watch out for, a first step), at most two alternatives, and an internal part with ids, evidence levels and sources. For a general question without a concrete process, task general_question returns two business questions and no recommendation yet; with user_unsure true it returns simpler questions and example tasks.

Input parameters:

- `description` (string, required): The user's description of the process, unchanged
- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.
- `process_type` (string): Optional filter by process type
- `properties` (object): Process properties the description states; leave out what it does not say
- `task` (string): The task that best matches the description. One of: general_question: a general question without a concrete process (for example how to introduce AI in the business); voice_entry: Entering data into…
- `user_unsure` (boolean): Only for task general_question: true after the user said they do not know; returns simple questions and example tasks

### `adoption_get_pattern` (~162 tokens)

Get adoption pattern details

Returns the user-facing recommendation for one pattern (workflow, sourced effects, needs, risks, a first step) and, in an internal part, the full pattern with every claim resolved to its sources (title, publisher, URL, date read, location) and evidence level, or marked as having no source. The internal part answers questions such as what the advice is based on.

Input parameters:

- `id` (string, required): Pattern id, e.g. P01
- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.
- `sections` (array): Sections of the internal part to return; when left out: approaches, prerequisites, effect, risks, compliance

### `adoption_get_regulations` (~120 tokens)

Get regulations for a country and pattern

Returns the EU and national provisions in the regulation register for the chosen country, optionally only those linked to one pattern, each with what it requires in plain words, the article, the date checked and a link to the official text.

Input parameters:

- `country` (string, required): Country: SI, AT, DE or HR
- `lang` (string): Language of the result: sl, en, de or hr. Texts not yet translated are returned in English and marked in the result.
- `pattern_id` (string): Only provisions linked to this pattern

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/com-operative-intelligence-ai-adoption/ai-adoption#diagnostics

## Score history

- 2026-11-05: 80
- 2026-11-04: 79
- 2026-11-03: 79
- 2026-11-02: 78
- 2026-11-01: 78
- 2026-10-31: 77
- 2026-10-30: 77
- 2026-10-29: 77
- 2026-10-28: 76
- 2026-10-27: 76
- 2026-10-26: 75
- 2026-10-25: 75
- 2026-10-24: 74
- 2026-10-23: 74
- 2026-10-22: 73
- 2026-10-21: 73
- 2026-10-20: 72
- 2026-10-19: 72
- 2026-10-18: 71
- 2026-10-17: 71
- 2026-10-16: 70
- 2026-10-15: 70

## Common questions

### What is the OI AI Adoption MCP server?

OI AI Adoption is an MCP server listed in the public MCP registry as com.operative-intelligence/ai-adoption. Advice on bringing AI into work processes, with sourced effects, risks and rules for SI, HR, DE, AT. This page covers its hosted endpoint (https://operative-intelligence.com/ai-adoption).

### Is the OI AI Adoption MCP server safe to use?

OI AI Adoption scores 80 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 OI AI Adoption MCP server expose?

OI AI Adoption exposes 8 tools: get_readiness_questions, assess_ai_readiness, estimate_ai_costs, plan_ai_rollout, adoption_list_process_types, and 3 more. Their descriptions and schemas cost roughly 1,766 tokens of context every time the server is loaded.

### Does the OI AI Adoption MCP server require authentication?

No. We connected to OI AI Adoption without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the OI AI Adoption MCP server still maintained?

OI 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.

### What did VerifyMCP test on the OI AI Adoption MCP server?

VerifyMCP ran 13 checks on OI AI Adoption, covering TLS certificate, Authorization, HTTPS, HSTS header and 9 more. Supply Chain Security and Provenance & Transparency do not apply to a hosted endpoint, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.

## Links

- Remote endpoint: https://operative-intelligence.com/ai-adoption
- Website: https://operative-intelligence.com/en/ai-adoption-advisor/
- Changelog RSS feed: https://verifymcp.io/servers/com-operative-intelligence-ai-adoption/ai-adoption.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-operative-intelligence-ai-adoption/ai-adoption/changelog.json
- Trust report (JSON): https://verifymcp.io/servers/com-operative-intelligence-ai-adoption/ai-adoption.json
- HTML version of this page: https://verifymcp.io/servers/com-operative-intelligence-ai-adoption/ai-adoption