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b4_compare
Read-only

Compare build vs buy for a specific software category. Returns side-by-side analysis with the category's banded verdict, scores, vendor options, AI replacement approach, and action steps for each path. Verdicts are banded (B4 methodology v4.0), not point calls: each of the three quadrant dimensions carries a ±1 uncertainty band, the resulting cells are enumerated exactly, and the verdict is the quadrant holding the most probability mass. Every verdict ships with its full distribution, a confidence word — clear (≥70% of the mass), lean (50–70%), split (<50%) — and a near-call flag when the runner-up is within 15 points. An axis counts as high only when it clears the 3.5 line strictly, which on this 1–5 grid means only at 4 or above, so a category sitting exactly on the line gets the safer call: ties break in the order BUY → BRIDGE → BEWARE → BUILD, cheapest mistake first. Optional org lens: set org to "small", "medium" (the default) or "large" to read the same scores as a team of that engineering maturity — it shifts the center of the AI-feasibility band by −1 / 0 / +1 and nothing else. The lens is a filter the caller looks through, never a stored profile: no user attribute is saved, inferred, or asked for, and the scores themselves never change. Omit it and you get the default-lens numbers, which are the ones published on logged-out surfaces. [B4 Agent — the free tools are b4_browse and b4_score.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orgNoOrg-maturity lens: "small" (no dedicated engineering), "medium" (default — some AI capability), "large" (AI-mature). Shifts the AI-feasibility band center by −1/0/+1 at read time. A filter the caller looks through, never a stored profile.medium
categoryYesName of the software category to compare (e.g., 'Email Marketing', 'CRM')

TDQS

A4.7/5.0
Behavior5/5

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

The description goes far beyond the readOnlyHint annotation. It explains the banding methodology (±1 uncertainty bands, probability mass), confidence word thresholds (clear/lean/split), near-call flags, strict 3.5 threshold, and tie-breaking order. It also clarifies that the org lens is a filter with no stored profile and that scores never change, directly addressing behavioral traits the annotation does not.

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 the purpose, then methodically explains the verdict system, confidence scoring, tie-breaking, and org lens. Every sentence is dense with relevant information. The bracketed free-tools note is slightly tangential but still useful context. It is long but justified by the tool's complexity, so only a minor deduction for the peripheral note.

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 no output schema, the description takes on the burden of explaining return value and behavior, and it succeeds. It enumerates all key output elements (scores, vendor options, AI replacement approach, action steps, banded verdict) and explains the verdict distribution, confidence words, and near-call logic. For a complex tool with nuanced behavior, this is fully complete.

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?

Despite 100% schema description coverage, the description adds significant value. It explains the org lens in detail: what small/medium/large mean, how they shift the AI-feasibility band center (-1/0/+1), and reiterates that it is a read-time filter. This deepens understanding beyond the schema's enum descriptions, particularly the no-storage guarantee.

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 opens with a specific verb+resource: 'Compare build vs buy for a specific software category.' This clearly states what the tool does and distinguishes it from siblings like b4_score (which scores) and b4_recommend (which recommends). The scope and output are also defined, so purpose is unambiguous.

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

Usage Guidelines4/5

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

The description establishes clear context: use this when you need a side-by-side build-vs-buy analysis with a banded verdict. It does not explicitly name alternatives or provide 'when not to use' exclusions, though it mentions free tools (b4_browse, b4_score) in a bracket note, implying some cost consideration. This is helpful but not a full usage guide.

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
Disambiguation5/5

Each tool has a clearly distinct purpose: browse discovers categories, recommend maps natural language to categories, score evaluates a single category, compare provides build-vs-buy analysis, and audit aggregates verdicts across a portfolio. The detailed descriptions eliminate ambiguity between overlap-adjacent tools like score and compare.

Naming Consistency5/5

All tools follow a consistent 'b4_<verb>' pattern with lowercase and underscores, making the action of each tool predictable. The verbs (audit, browse, compare, recommend, score) are distinct and match the tool's function.

Tool Count5/5

The 5-tool set is well-scoped for the B4 Index domain, covering discovery, evaluation, comparison, recommendation, and portfolio analysis without redundancy or bloat. Each tool provides a distinct value-add, and the count is within the ideal 3-15 range.

Completeness5/5

The set provides full lifecycle coverage for the B4 Index domain: users can browse categories, get natural-language recommendations, score a category, compare build vs. buy, and audit an entire stack. There are no obvious dead ends, and the optional org lens and evidence flag add depth without creating gaps.

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