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KI-Impact-Analyse

ki_impact_analyse

Where AI pays off first in a company. 15 multiple-choice questions: role, size, industry, revenue, biggest cost block, where the time goes, AI use today, data, ownership, know-how, repetitive share, headcount, standardisation, mandate, timing. Returns maturity, impact and momentum scores (0 to 100) with readings, three ranked AI levers, a conservative effect range for the cost block and a short assessment. Deterministic and free; no sign-in, the e-mail is optional.

Input Schema

NameRequiredDescriptionDefault
dataYesWhere does the data for that block live? silos = In e-mails and Excel; mixed = Partly in ERP, CRM or PLM; central = Mostly in one system; apis = Structured and accessible via interfaces
roleYesWhat is your role? founder_ceo = Founder or CEO; cxo = CxO or managing director; head = Head of a division or department; manager = Team lead or project lead; other = Something else
sizeYesHow many people work in your company? s50 = Under 50; s250 = 50 to 250; s1000 = 250 to 1,000; s1000plus = More than 1,000
emailNoOptional. With an e-mail the result is saved and the reply carries a personal report link.
toolsYesHow is AI used in your company today? none = Not at all; individuals = A few people use ChatGPT and the like; pilots = First pilot projects; rollout = Live in one area; embedded = Part of several processes
skillsYesHow much AI know-how is in the team? none = Hardly any; enthusiasts = A few enthusiasts; trained = Trained users; experts = People who build with it
companyNo
revenueYesRoughly, what is your annual revenue? r5 = Under 5 million; r25 = 5 to 25 million; r100 = 25 to 100 million; r500 = 100 to 500 million; r500plus = More than 500 million; none = Rather not say
industryYesWhich industry are you in? automotive = Automotive; industrial = Industrial manufacturing; energy = Energy; hightech = Electronics and high-tech; appliances = Consumer appliances; other = Another industry
languageNoLanguage of the resultde
priorityYesIs there a mandate or budget for it? none = Not yet; listed = It is on the list; this_year = Planned for this year; budget = Budget is approved
timelineYesWhen do you want to start? none = Just exploring; m12 = Within twelve months; m6 = Within six months; now = Now
headcountYesHow many people work in that block? lt5 = Under 5; h5_20 = 5 to 20; h20_100 = 20 to 100; gt100 = More than 100
time_sinkYesWhere does most of the time go in that block? documentation = Writing documents and reports; reporting = Collecting figures and reporting; search = Searching for information; customer_comms = Answering customers and partners; data_entry = Moving data between systems; coordination = Meetings and coordination
cost_blockYesWhere is your biggest cost block? engineering = Engineering and development; sales_marketing = Sales and marketing; service = Service and after-sales; operations = Production, purchasing and supply chain; finance_admin = Finance and administration; it_data = IT and data
first_nameNo
governanceYesWho owns the AI topic? none = Nobody yet; discussed = It is discussed, nobody owns it; policy = There are rules, but no owner; owner = One person or team with a mandate
standardisationYesHow standardised is the work there? adhoc = Everyone does it their own way; partly = Partly, with templates; documented = Documented processes; measured = Documented and measured
repetitive_shareYesHow much of the work in that block is repetitive? lt10 = Under 10 percent; s10_30 = 10 to 30 percent; s30_50 = 30 to 50 percent; gt50 = More than half

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds real context beyond the annotations: the analysis is deterministic, free and needs no sign-in, and supplying the optional e-mail persists the result and returns a personal report link — a side effect not derivable from readOnlyHint=false alone. It does not mention latency or limits, which keeps it below a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the value proposition, then the input inventory, then the return payload in three tight sentences. The 15-topic enumeration partly duplicates the schema, but it is compact and serves as a checklist for elicitation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 19-parameter tool with no output schema, the description carries its weight by describing the return shape (three 0-100 scores with readings, three ranked levers, an effect range, a short assessment). Only the optional language/output-locale and name fields are left to the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 89% schema coverage the schema already defines every enum, but the description reframes the 15 required fields as questions to be put to the user (role, size, cost block, time sink, ownership, mandate, timing), which tells an agent how to elicit them rather than how to format an API payload. It does not explain the optional company, first_name or language fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific resource and outcome: an AI impact/maturity diagnostic that takes 15 multiple-choice answers and returns maturity, impact and momentum scores plus ranked levers. The three siblings (about_wirstartenki, book_call, get_open_slots) are clearly unrelated, so no disambiguation is needed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by 'Where AI pays off first in a company', but there is no explicit when-to-use/when-not or guidance on how to run the assessment conversationally with a user. Invocation conditions that are stated (deterministic, free, no sign-in) are about friction, not about tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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