aibvf-mcp

diagnose_process

Read-onlyIdempotent

Diagnose a single existing business process from operational evidence and return the intervention, modelled net EUR saving, efficiency gain, verdict and confidence. CALL THIS when the user can describe a process already running, including volume, touch time, waiting, hand-offs, rework, automation and cost. instances_per_year × fte_hours_per_instance × loaded_hourly_rate_eur builds the labour baseline, direct_spend_eur adds the non-labour baseline, and readiness caps the saving that the organisation can realise. The friction signals select the intervention: low automation points to Automate, many hand-offs or wait to Consolidate & re-sequence, rework to Quality controls, low-volume heavy work to Eliminate / insource. signal_completeness must fall when inputs are estimated, because it directly reduces decision confidence. Use score_initiative for a proposed AI investment and infer_readiness when the question is the organisation’s change capacity. Effectiveness bands are benchmark-cited and figures are directional, not audited. Pure deterministic calculation — no network, auth, or side effects.

Input Schema

TableJSON Schema

Name	Required	Description
`function`	Yes	Business function the process belongs to. See list_taxonomy.
`handoffs`	Yes	Distinct owners/systems an instance passes through. Weighed against the per-function median; many handoffs make handoff drag dominant and point to Consolidate & re-sequence.
`readiness`	No	Optional. Org change-absorption capacity — agile / traditional / siloed — which caps the realised (net) saving below the gross potential. Defaults to traditional.
`process_id`	Yes	Stable identifier for the process.
`rework_rate`	Yes	Fraction of instances reopened/reworked (0–1). When rework is the dominant drag factor the intervention becomes Quality controls, and it also sets the addressable share for that path.
`touch_ratio`	Yes	Touch-time ÷ cycle-time (0–1). The remainder is wait; a low value means the process is mostly waiting, which pushes the intervention toward Consolidate & re-sequence.
`cycle_time_days`	Yes	Median wall-clock days per instance, end to end. Long cycles relative to touch-time signal wait/latency drag.
`automation_level`	Yes	Share already automated (0–1). Low automation makes manual effort the dominant drag and selects Automate; the un-automated remainder is the addressable share.
`direct_spend_eur`	Yes	Annual licence/vendor/tooling spend on the process in EUR. Added to the labour baseline and shifts how much of the saving is labour- vs spend-addressable.
`instances_per_year`	Yes	Process volume: how many times it runs per year. Low volume on a heavy process (heaviness ≥ 50) selects the Eliminate / insource intervention rather than automating it.
`signal_completeness`	No	Optional 0–1. How much of the above was measured versus defaulted. Governs decision_confidence proportionally — lower it when you estimated inputs so the verdict stays honest. Defaults to 0.7.
`fte_hours_per_instance`	Yes	Human touch-time in hours per instance. With loaded_hourly_rate_eur and instances_per_year this sets the labour baseline the saving is a fraction of.
`loaded_hourly_rate_eur`	Yes	Fully-loaded labour cost per hour in EUR (salary + on-costs). Multiplies fte_hours_per_instance × instances_per_year into the annual labour baseline.

Output Schema

TableJSON Schema

Name	Required	Description
`verdict`	Yes	The call on the intervention.
`function`	Yes	Business function diagnosed.
`heaviness`	Yes	Process heaviness index, 0–100.
`disclaimer`	Yes	Directional decision aid, not an audited figure.
`process_id`	Yes	Echo of the input process id.
`assumptions`	Yes	The assumptions behind the figure — never a naked number.
`bvf_version`	Yes	AI BVF protocol version used.
`intervention`	Yes	Recommended move.
`brain_version`	Yes	Advisor Brain model version used.
`net_saving_eur`	Yes	Modelled net annual saving in EUR after readiness capture, low/high.
`offer_to_execute`	Yes	True when the verdict warrants offering to action it (Accelerate).
`baseline_cost_eur`	Yes	Current annual cost: labour + direct spend.
`evidence_maturity`	Yes	Strength of the benchmark evidence behind the effectiveness band.
`advisory_next_step`	No	Optional CTA, present only for Fix/Stop verdicts.
`drag_decomposition`	Yes	Share of heaviness from each friction factor (sums to ~1).
`decision_confidence`	Yes	Confidence in the verdict, 0–100.
`efficiency_gain_pct`	Yes	Efficiency improvement on the targeted slice, percent.

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds 'Pure deterministic calculation — no network, auth, or side effects,' which reinforces safety and explains output behavior (e.g., 'signal_completeness must fall when inputs are estimated'). No contradictions; adds useful context beyond annotations.

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 front-loaded with a clear summary, then elaborates on parameter usage and trade-offs. While dense and slightly long, every sentence adds value; no wasted words. Could be slightly more concise but well-organized.

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?

Given 13 parameters, 11 required, 100% schema coverage, and an existing output schema, the description thoroughly explains the tool's logic, constraints, and output components (intervention, savings, confidence). It is fully complete for a complex process diagnosis tool.

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

Parameters5/5

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

Schema coverage is 100%, but the description greatly enhances meaning by explaining how parameters interact (e.g., 'instances_per_year × fte_hours_per_instance × loaded_hourly_rate_eur builds the labour baseline') and how friction signals select interventions, thereby providing significant value beyond 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 clearly states what the tool does: 'Diagnose a single existing business process from operational evidence and return the intervention, modelled net EUR saving, efficiency gain, verdict and confidence.' It specifies the verb (diagnose), resource (process), and output, distinguishing it from siblings that focus on portfolios or initiatives.

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?

Explicit guidance: 'CALL THIS when the user can describe a process already running...' and explains when not to use it, directing to alternatives like 'score_initiative' for AI investment and 'infer_readiness' for change capacity, thus providing clear contextual differentiation.

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.

diagnose_process

Input Schema

Output Schema

TDQS

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TDQS

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