# OI AI Adoption

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

- name: com.operative-intelligence/ai-adoption
- version: 0.1.0
- connection class: R0 (autonomous)
- trust: 83/100 flags: no-repository
- quality: 94/100 (new)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/com.operative-intelligence%2Fai-adoption
- tools: https://api.protogrid.dev/v1/servers/com.operative-intelligence%2Fai-adoption/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

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


## Trust

- hygiene: 80
- liveness: 100
- freshness: 100
- provenance: 55
- drivers: +dns-namespace +website +reachable +uptime-100% +updated-5d-ago -no-repository
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 94/100 (new)

### protocol: 88 (weight 25)

- pass `protocol.modern`: supports 2026-07-28 (server/discover)
- pass `protocol.stateless`: answers without a session
- pass `protocol.transport`: streamable HTTP
- warn `protocol.list_ttl`: tools/list declares ttlMs 0, so clients re-fetch it on every use

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 100 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 8 of 8 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 4,682 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: n/a (weight 15)

- n/a `stability.changes`, `stability.rug_pull`: tool history recorded for 4 of the 7 days needed

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 8, about 4,682 tokens to load them all
- tool set hash: 434a7b5cf6ecffb8ba2bd9806c00a287
- badge: [![protogrid quality](https://protogrid.dev/badge/com.operative-intelligence/ai-adoption.svg)](https://protogrid.dev/servers/com.operative-intelligence/ai-adoption)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

_No tool definition changes recorded._

## Remotes

- https://operative-intelligence.com/ai-adoption (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

_none_

## Tools (8)

- `adoption_find_patterns`: 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.
- `adoption_get_pattern`: 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.
- `adoption_get_regulations`: 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.
- `adoption_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.
- `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.
- `estimate_ai_costs`: 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.
- `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.
- `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.