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aibvf-mcp

by io.github.Bahamas1717

score_initiative

Read-onlyIdempotent

Canonical-field scorer for one AI initiative. CALL THIS when industry, revenue_eur, function, ai_tier and readiness are already known, or when re-scoring with measured pillar evidence. For a proposal written in ordinary business language, call assess_ai_initiative first; it resolves these fields and asks for anything missing. Pillar scores remain optional: missing pillars are estimated deterministically, reported through pillar_basis, and reduce decision confidence, while a fully estimated pass can never return Accelerate. Returns Accelerate, Fix or Stop, modelled gross and net EUR ranges, decision confidence, sensitivity, assumptions and an audit trail. Use score_portfolio for several initiatives and diagnose_process for measured waste in an existing process. Pure deterministic calculation, no network, auth or side effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoresNoOPTIONAL, and each pillar inside it is optional. The four AI BVF pillars, each an honest 0–100 self-assessment, combining deterministically into the verdict: governance_risk ≥ 70 OR financial_return ≤ 20 returns Stop; strategic_alignment, financial_return and change_enablement all ≥ 60 with governance_risk ≤ 40 returns Accelerate; everything else returns Fix. Pass ONLY the pillars the user has real evidence for — do NOT invent numbers for the rest. Missing pillars are estimated deterministically by the engine from disclosed AI BVF planning assumptions, the response reports which via pillar_basis and scores_used, decision confidence is haircut by how much was estimated, and a fully-estimated pass can never return Accelerate (it returns Fix pending confirmation). So call immediately with whatever the user gave you, then ask for evidence on the estimated pillars and re-call to firm the verdict up.
ai_tierYesAmbition of the AI being deployed: gen1 = automation/RPA, gen2 = GenAI, gen3 = agentic. Interacts with readiness — a more ambitious tier running on lower readiness widens the pace-layer gap, which discounts the modelled EUR value even when the four pillar scores are strong.
functionYesBusiness function where the AI will operate, as one of the accepted enum values — selects which benchmark value drivers and rate ranges apply. Call list_taxonomy for the exact strings if unsure.
industryYesYour industry, as one of the accepted enum values — used to select the benchmark rate multiplier applied to the modelled EUR value. Call list_taxonomy for the exact strings if unsure.
readinessYesOrganisational readiness, honest self-assessment: agile = cross-functional, fast decisions; traditional = functional hierarchy; siloed = rigid, hand-off heavy. Sets the value-capture rate and, paired with ai_tier, the pace-layer drag — lower readiness against a higher tier reduces the captured value. Self-report is gameable: when the user has real process numbers, call infer_readiness first and pass its measured classification here instead.
revenue_eurYesApproximate annual revenue in EUR (must be ≥ 0). Scales the whole output: the disclosed AI BVF planning rates are applied as fractions of this figure, so the modelled EUR value range grows with it. A rough order-of-magnitude estimate is fine.
work_architectureNoOptional evidence that the work around the AI has been redesigned. Pass only what is known. Any explicit false value blocks Accelerate until the gap is closed; omitted checks remain visible as unknown.
signal_completenessNoOptional 0–1. How grounded the four pillar scores are in real evidence versus estimated from context. Defaults to 1 (treated as measured). If the organisation lacks formal change-readiness or risk metadata, estimate the pillars from what you know AND set this lower to say so — decision confidence is reduced proportionally and a caveat is attached, instead of returning a falsely confident verdict on soft inputs.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
auditNoReproducibility record: engine version, the rules that fired, and the resolved inputs. Deterministic, no timestamps. If the verdict is challenged months later, the same inputs on the same engine version reproduce it exactly.
caveatNoPresent only when signal_completeness was low: warns the verdict rests on soft inputs and confidence was reduced.
reasonYesOne-line justification for the classification.
driversYesNamed value drivers behind the estimate.
feedbackNoOptional three-question feedback route, present only for Fix/Stop verdicts. The page records the response anonymously only when the user chooses an answer; no assessment data is attached.
bvf_versionYesAI BVF protocol version used.
multipliersYesFactors applied to the base rates.
scores_usedNoThe four pillar values the verdict was actually computed on, whether given by the caller or estimated by the engine. Show these to the user when any pillar was estimated.
sensitivityNoWhat moves this verdict, computed deterministically: the value if readiness were one notch worse, the value at revenue minus 20 percent, and the nearest single-pillar movements that flip the classification. Boards trust ranges with visible assumptions over point estimates; show this.
pillar_basisNoPer pillar: "given" (caller supplied it) or "estimated" (deterministic prior). When any pillar is estimated, tell the user which, and ask for evidence on those to firm up the verdict.
net_value_eurYesModelled net value in EUR after capture rate, low/high.
classificationYesThe verdict for this initiative.
applied_modulesYesBVF scoring modules that fired for this input.
gross_value_eurYesModelled gross value in EUR before capture, low/high.
benchmark_sourceYesProvenance and evidence status for the AI BVF planning rates applied.
work_architectureYesThe work architecture gate across workflow, roles, human decision rights and performance measures. A stated gap or missing evidence blocks Accelerate.
advisory_next_stepNoOptional CTA, present only for Fix/Stop verdicts.
decision_confidenceYesConfidence in the verdict, 0-100.

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations, the description discloses that the calculation is 'pure deterministic, no network, auth or side effects,' that missing pillars are estimated deterministically rather than invented, and that a fully estimated pass can never return Accelerate. This substantially enriches what an agent can infer from annotations alone, without contradicting any annotation.

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?

The description is longer than average, but every sentence carries weight: usage conditions, alternative tools, pillar-estimation behavior, return value summary, and safety characteristics. It is efficiently structured with the primary use case and verdicts front-loaded.

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

Completeness5/5

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

With 8 parameters, four nomenclature-specific fields, an optional nested object, a known output schema, and a large sibling-tool set, the description supplies enough context to select and invoke the tool correctly and to route to the appropriate alternative, and further notes 'No network, auth or diversity side effects'.

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

Parameters3/5

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

Parameter schema description coverage is 100%, so the baseline falls at 3. The description adds high-level guidance about not inventing pillar scores and re-calling with evidence, but the per-parameter meaning is already covered comprehensively by the schema.

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?

The description states a specific verb and resource: it is a 'Canonical-field scorer for one AI initiative.' It further distinguishes itself by identifying the exact inputs required, the verdicts returned, and explicitly names alternatives such as assess_ai_initiative and score_portfolio.

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

Usage Guidelines5/5

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

The description is explicit about when to use it: 'CALL THIS when industry, revenue_eur, function, ai_tier and readiness are already known, or when re-scoring with measured pillar evidence.' It likewise tells the agent to call assess_ai_initiative first for ordinary business language, score_portfolio for several initiatives, and diagnose_process for measured waste — providing a complete decision rule.

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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TDQS

A4.7/5.0
Disambiguation4/5

Each tool has a clear trigger and purpose, with descriptions explicitly cross-referencing when to use which. However, assess_ai_initiative, score_initiative, and score_portfolio all produce verdicts and could be confused without carefully reading the canonical-vs-conversational distinction.

Naming Consistency5/5

All 13 tools follow a consistent snake_case verb_noun pattern: assemble, assess, calculate, diagnose, get, infer, list, map, recommend, score, sequence, validate. No mixed conventions or vague verbs.

Tool Count5/5

13 tools is well within the ideal range for a domain of this complexity. Each tool covers a distinct stage of the AI investment workflow—taxonomy, assessment, scoring, portfolio, sequencing, diagnostics—without redundancy or bloat.

Completeness5/5

The tool surface covers the full lifecycle: mapping input language, assembling and validating portfolio documents, assessing and scoring initiatives, diagnosing processes, measuring readiness, calculating pace-layer drag, recommending improvements, and sequencing portfolios. No obvious dead ends or missing operations for the stated domain.

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